Array processing: Difference between revisions

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{{distinguish|Array processor|Array data structure}}

{{more footnotes|date=November 2012}}

'''Array processing''': [[signal processing]] is a wide area of research that extends from the simplest form of 1-D signaldimensional processingline arrays to the2 complexand form3 dimensional array geometries. Array structure can be defined as a set of M-Dsensors that are spatially separated, e.g. [[antenna (radio)|radio antenna]] and [[Seismic array|seimic signal processingarrays]]. ThisThe sensors articleused presentsfor a shortspecific surveyproblem ofmay thevary conceptswidely, principlesfor andexample applications[[microphones|microphone]], of[[Accelerometer|accelerometers]] Arrayand Processing[[telescopes|telescope]]. ArrayHowever, structuremany cansimularities exist, the most fundamental of which may be definedan asassumption of [[wave propagation]]. By creating a setphysical model of sensorsthe thatwave arepropagation, spatiallyor separated,in e.g.[[machine antennas.learning]] Theapplications basica problem[[training thatdata]] weset, intendthe tospatial solvecoherence byof usingthe arraysignal processingreceived technique(s)on ismany to:sensors can be leveraged for many applications.

* Determine number and locations of energy-radiating sources (emitters).

Some basic problem that are solved with array processing techniques are:

* Enhance the signal to noise ratio SNR "[[SINR|signal-to-interference-plus-noise ratio (SINR)]]".

* Trackdetermine multiplenumber movingand locations of energy-radiating sources.

* Enhanceenhance the signal to noise ratio SNR "[[SINR|signal-to-interference-plus-noise ratio (SINR)]]".

Precisely, we are interested in solving these problems in noisy environments (in the presence of noise and interfering signals). [[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.

* track moving sources

* seperation of multiple sources

Precisely,Array processing wemetrics are interestedoften inassesed solvingnoisy theseenvironments, problemsthough inthis noisynoise environmentsmay (inbe theeither presencespatially ofincoherent noise andor other interfering signals) following the same propgation 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 arrival]]s, and their signal [[waveforms]].<ref name="utexas1">Torlak, M. [http://users.ece.utexas.edu/~bevans/courses/ee381k/lectures/13_Array_Processing/lecture13/lecture13.pdf Spatial Array Processing]. Signal and Image Processing Seminar. University of Texas at Austin.</ref><ref name="ref1">{{cite book|last=J Li|first=[[Peter Stoica]] (Eds)|title=MIMO Radar Signal Processing|year=2009|publisher=J Wiley&Sons|___location=USA}}</ref><ref name="ref2">{{cite book|last=[[Peter Stoica]]|first=R Moses|title=Spectral Analysis of Signals|year=2005|publisher=Prentice Hall|___location=NJ|url=http://user.it.uu.se/%7Eps/SAS-new.pdf}}</ref><ref name="ref3">{{cite book|last=J Li|first=[[Peter Stoica]] (Eds)|title=Robust Adaptive Beamforming|year=2006|publisher=J Wiley&Sons|___location=USA}}</ref>

[[File:Aray Prcessing Model.png|thumb|Sensors array]]