Введение
Область исследований в области обработки сигналов
Area of research in signal processing
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Обработка массивов – это широкая область исследований в области обработки сигналов, охватывающая варианты от простейших одномерных линейных массивов до двумерных и трехмерных геометрических конфигураций. Структура массива определяется как набор пространственно разделенных сенсоров, например, радиоантенн и сейсмических массивов. Сенсоры, используемые для конкретной задачи, могут значительно различаться, включая микрофоны, акселерометры и телескопы. Однако существует множество общих черт, наиболее фундаментальной из которых является предположение о распространении волн. Распространение волн подразумевает наличие систематической связи между сигналами, принимаемыми пространственно разделенными сенсорами. Создавая физическую модель распространения волн или, в приложениях машинного обучения, набор обучающих данных, можно использовать взаимосвязи между сигналами, полученными на пространственно разделенных сенсорах, для решения широкого круга задач. Некоторые распространенные задачи, решаемые с помощью методов обработки массивов:
определение количества и местоположения источников излучения энергии;
улучшение отношения сигнал/шум (SNR) или отношения сигнал/помехи плюс шум (SINR);
отслеживание движущихся источников.
Area of research in signal processing
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Метрики обработки массивов часто оцениваются в зашумленных условиях. Модель шума может предполагать пространственно некоррелированный шум или наличие интерферирующих сигналов, подчиняющихся тем же законам распространения. Теория оценок является важной и базовой частью области обработки сигналов, используемой для решения задач оценки, в которых значения нескольких параметров системы должны быть оценены на основе измеренных/эмпирических данных, содержащих случайную составляющую. С увеличением числа приложений оценка временных и пространственных параметров становится все более важной. Обработка массивов активно развивалась в последние десятилетия, опираясь на возможность использования и объединения данных с различных сенсоров (антенн) для решения конкретных задач оценки (пространственной и временной обработки). Помимо информации, извлекаемой из собранных данных, в рамках этой области используется априорное знание о геометрии массива сенсоров для выполнения задачи оценки. Обработка массивов применяется в радиолокации, гидролокации, сейсмической разведке, системах противодействия помехам и беспроводной связи. Одним из основных преимуществ использования обработки массивов вместе с массивом сенсоров является уменьшение габаритов. Проблемы, связанные с обработкой массивов, включают количество источников, направление их прихода и форму сигналов. Существует четыре основных предположения в обработке массивов. Первое предположение заключается в том, что распространение во всех направлениях является однородным в изотропной и недиспергирующей среде. Второе предположение состоит в том, что для обработки массивов в дальней зоне радиус распространения значительно превышает размер массива и происходит распространение плоских волн. Третье предположение заключается в том, что шум и сигнал имеют нулевое среднее значение и некоррелированы. Наконец, последнее предположение состоит в том, что отсутствует связь между элементами массива и калибровка выполнена идеально. NORSAR – это независимая геофизическая исследовательская организация, основанная в Норвегии в 1968 году. С тех пор NORSAR занимается обработкой массивов для измерения сейсмической активности по всему миру. В настоящее время они работают над Международной системой мониторинга, которая будет состоять из 50 основных и 120 вспомогательных сейсмических станций по всему миру. NORSAR продолжает работу по совершенствованию обработки массивов для повышения точности мониторинга сейсмической активности не только в Норвегии, но и во всем мире. Связь (беспроводная)
Area of research in signal processing
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Связь определяется как процесс обмена информацией между двумя или более сторонами. Последние два десятилетия характеризуются стремительным ростом систем беспроводной связи. Этот успех является результатом достижений в теории связи и разработке малопотребляющих устройств. В общем случае связь (телекоммуникация) может осуществляться технологическими средствами посредством электрических сигналов (проводная связь) или электромагнитных волн (беспроводная связь). Антенные массивы стали вспомогательной технологией для повышения эффективности использования спектра и повышения точности систем беспроводной связи за счет использования пространственного измерения в дополнение к классическим временным и частотным измерениям. Методы обработки массивов и оценки используются в беспроводной связи. В течение последнего десятилетия эти методы были переосмыслены как перспективные решения для многочисленных проблем в беспроводной связи. В беспроводной связи проблемы, влияющие на качество и производительность системы, могут возникать из различных источников. Модель связи с множественным доступом с разделением по коду и многолучевым распространением сигнала по множеству рассеивающих путей является одной из наиболее распространенных моделей связи в беспроводной связи (мобильной связи). В случае многопользовательской среды наличие нескольких пользователей увеличивает вероятность межпользовательских помех, которые могут негативно повлиять на качество и производительность системы. В системах мобильной связи многолучевость является одной из основных проблем, с которыми приходится сталкиваться базовым станциям. Базовые станции используют пространственное разнесение для борьбы с замираниями, вызванными многолучевостью. Базовые станции используют антенный массив из нескольких элементов для достижения большей избирательности, так называемого формирования луча. Принимающий массив может быть направлен в сторону одного пользователя за раз, избегая при этом помех от других пользователей. Медицинские применения
Area of research in signal processing
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Методы обработки массивов привлекли значительное внимание со стороны медицинских и промышленных приложений. В медицине область обработки медицинских изображений была одной из основных областей применения обработки массивов. Другие медицинские применения обработки массивов: лечение заболеваний, отслеживание сигналов, содержащих информацию о состоянии внутренних органов, например, сердца, локализация и анализ активности мозга с использованием биомагнитных массивов сенсоров. Обработка массивов для улучшения речи
Area of research in signal processing
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Улучшение и обработка речи – еще одна область, на которую повлияла новая эра обработки массивов. Большинство акустических фронтенд-систем стали полностью автоматическими (например, телефоны). Однако рабочая среда этих систем содержит смесь других акустических источников; внешние шумы, а также акустическая связь сигналов громкоговорителей заглушают и ослабляют желаемый речевой сигнал. Помимо этих внешних источников, сила желаемого сигнала уменьшается из-за относительно большого расстояния между говорящим и микрофонами. Методы обработки массивов открыли новые возможности в обработке речи для подавления шума и эха без ухудшения качества и негативного влияния на речевой сигнал. В общем, методы обработки массивов могут использоваться в обработке речи для уменьшения вычислительной мощности (количества вычислений) и повышения качества системы (производительности). Представление сигнала в виде суммы поддиапазонов и адаптация фильтров подавления для поддиапазонных сигналов может уменьшить требуемую вычислительную мощность и привести к системе с более высокой производительностью. Опора на несколько входных каналов позволяет разрабатывать системы более высокого качества по сравнению с системами, использующими один канал, и решать такие задачи, как локализация, отслеживание и разделение источников, которые невозможно решить при использовании одного канала. Обработка массивов в астрономических приложениях
Area of research in signal processing
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Астрономическая среда содержит смесь внешних сигналов и шумов, которые влияют на качество желаемых сигналов. Большинство приложений обработки массивов в астрономии связаны с обработкой изображений. Массив используется для достижения более высокого качества, которое невозможно получить при использовании одного канала. Высокое качество изображения облегчает количественный анализ и сравнение с изображениями на других длинах волн. В общем, астрономические массивы можно разделить на два класса: класс формирования луча и класс корреляции. Формирование луча – это сигнал п…
Area of research in signal processing
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e. g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications. Some common problem that are solved with array processing techniques are:
determine number and locations of energy radiating sources
enhance the signal to noise ratio (SNR) or "signal to interference plus noise ratio (SINR)"
track moving sources
Array processing metrics are often assessed noisy environments. The model for noise may be either one of spatially incoherent noise, or one with interfering signals following the same propagation physics. Estimation theory is an important and basic part of signal processing field, which used to deal with estimation problem in which the values of several parameters of the system should be estimated based on measured/empirical data that has a random component. As the number of applications increases, estimating temporal and spatial parameters become more important. Array processing emerged in the last few decades as an active area and was centered on the ability of using and combining data from different sensors (antennas) in order to deal with specific estimation task (spatial and temporal processing). In addition to the information that can be extracted from the collected data the framework uses the advantage prior knowledge about the geometry of the sensor array to perform the estimation task. Array processing is used in radar, sonar, seismic exploration, anti jamming and wireless communications. One of the main advantages of using array processing along with an array of sensors is a smaller foot print. The problems associated with array processing include the number of sources used, their direction of arrivals, and their signal waveforms. There are four assumptions in array processing. The first assumption is that there is uniform propagation in all directions of isotropic and non dispersive medium. The second assumption is that for far field array processing, the radius of propagation is much greater than size of the array and that there is plane wave propagation. The third assumption is that there is a zero mean white noise and signal, which shows uncorrelation. Finally, the last assumption is that there is no coupling and the calibration is perfect. NORSAR is an independent geo scientific research facility that was founded in Norway in 1968. NORSAR has been working with array processing ever since to measure seismic activity around the globe. They are currently working on an International Monitoring System which will comprise 50 primary and 120 auxiliary seismic stations around the world. NORSAR has ongoing work to improve array processing to improve monitoring of seismic activity not only in Norway but around the globe. Communications (wireless)
Communication can be defined as the process of exchanging of information between two or more parties. The last two decades witnessed a rapid growth of wireless communication systems. This success is a result of advances in communication theory and low power dissipation design process. In general, communication (telecommunication) can be done by technological means through either electrical signals (wired communication) or electromagnetic waves (wireless communication). Antenna arrays have emerged as a support technology to increase the usage efficiency of spectral and enhance the accuracy of wireless communication systems by utilizing spatial dimension in addition to the classical time and frequency dimensions. Array processing and estimation techniques have been used in wireless communication. During the last decade these techniques were re explored as ideal candidates to be the solution for numerous problems in wireless communication. In wireless communication, problems that affect quality and performance of the system may come from different sources. The multiuser –medium multiple access and multipath signal propagation over multiple scattering paths in wireless channels communication model is one of the most widespread communication models in wireless communication (mobile communication). In the case of multiuser communication environment, the existence of multiuser increases the inter user interference possibility that can affect quality and performance of the system adversely. In mobile communication systems the multipath problem is one of the basic problems that base stations have to deal with. Base stations have been using spatial diversity for combating fading due to the severe multipath. Base stations use an antenna array of several elements to achieve higher selectivity, so called beamforming. Receiving array can be directed in the direction of one user at a time, while avoiding the interference from other users. Medical applications
Array processing techniques got on much attention from medical and industrial applications. In medical applications, the medical image processing field was one of the basic fields that use array processing. Other medical applications that use array processing: diseases treatment, tracking waveforms that have information about the condition of internal organs e. g. the heart, localizing and analyzing brain activity by using bio magnetic sensor arrays. Array Processing for Speech Enhancement
Speech enhancement and processing represents another field that has been affected by the new era of array processing. Most of the acoustic front end systems became fully automatic systems (e. g. telephones). However, the operational environment of these systems contains a mix of other acoustic sources; external noises as well as acoustic couplings of loudspeaker signals overwhelm and attenuate the desired speech signal. In addition to these external sources, the strength of the desired signal is reduced due to the relatively distance between speaker and microphones. Array processing techniques have opened new opportunities in speech processing to attenuate noise and echo without degrading the quality of and affecting adversely the speech signal. In general array processing techniques can be used in speech processing to reduce the computing power (number of computations) and enhance the quality of the system (the performance). Representing the signal as a sum of sub bands and adapting cancellation filters for the sub band signals can reduce the demanded computation power and lead to a higher performance system. Relying on multiple input channels allows designing systems of higher quality comparing to systems that use single channel and solving problems such as source localization, tracking and separation, which cannot be achieved in case of using single channel. Array Processing in Astronomy Applications
Astronomical environment contains a mix of external signals and noises that affect the quality of the desired signals. Most of the arrays processing applications in astronomy are related to image processing. The array used to achieve a higher quality that is not achievable by using a single channel. The high image quality facilitates quantitative analysis and comparison with images at other wavelengths. In general, astronomy arrays can be divided into two classes: the beamforming class and the correlation class. Beamforming is a signal processing techniques that produce summed array beams from a direction of interest – used basically in directional signal transmission or reception the basic idea is to combine elements in a phased array such that some signals experience destructive inference and other experience constructive inference. Correlation arrays provide images over the entire single element primary beam pattern, computed off line from records of all the possible correlations between the antennas, pairwise. Other applications
In addition to these applications, many applications have been developed based on array processing techniques: Acoustic Beamforming for Hearing Aid Applications, Under determined Blind Source Separation Using Acoustic Arrays, Digital 3D/4D Ultrasound Imaging Array, Smart Antennas, Synthetic aperture radar, underwater acoustic imaging, and Chemical sensor arrays etc. Correlation spectrometers like the Michelson interferometer vary the time lag between signals obtain the power spectrum of input signals. The power spectrum of a signal is related to its autocorrelation function by a Fourier transform:
where the autocorrelation function for signal X as a function of time delay is
Cross correlation spectroscopy with spatial interferometry, is possible by simply substituting a signal with voltage in equation to produce the cross correlation and the cross spectrum .
Пример: пространственная фильтрация
В радиоастрономии необходимо подавлять радиочастотные помехи для обнаружения и наблюдения значимых объектов и явлений в ночном небе.
In radio astronomy, RF interference must be mitigated to detect and observe any meaningful objects and events in the night sky.
Резюме
Техника обработки массивов представляет собой прорыв в области обработки сигналов. Рассматривается широкий спектр приложений и задач, решаемых с использованием методов обработки массивов. Помимо этих приложений, в ближайшие несколько лет число приложений, включающих ту или иную форму обработки массивов сигналов, будет расти. Ожидается, что значимость обработки массивов возрастет по мере распространения автоматизации в промышленности и в различных приложениях, а дальнейшие достижения в области цифровой обработки сигналов и систем цифровой обработки сигналов также будут способствовать удовлетворению высоких вычислительных требований, предъявляемых некоторыми методами оценки. В данной статье подчеркивается важность обработки массивов путем перечисления наиболее значимых приложений, использующих методы обработки массивов. Кратко описываются различные классификации обработки массивов, основанные на спектральных и параметрических подходах. Освещаются некоторые из наиболее важных алгоритмов, а также объясняются и обсуждаются их преимущества и недостатки.
Array processing technique represents a breakthrough in signal processing. Many applications and problems which are solvable using array processing techniques are introduced. In addition to these applications within the next few years the number of applications that include a form of array signal processing will increase. It is highly expected that the importance of array processing will grow as the automation becomes more common in industrial environment and applications, further advances in digital signal processing and digital signal processing systems will also support the high computation requirements demanded by some of the estimation techniques. In this article we emphasized the importance of array processing by listing the most important applications that include a form of array processing techniques. We briefly describe the different classifications of array processing, spectral and parametric based approaches. Some of the most important algorithms are covered, the advantage(s) and the disadvantage(s) of these algorithms also explained and discussed.