Array processing: Difference between revisions

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

{{more footnotes|date=November 2012}}

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. [[antenna (radio)|radio antenna]] and [[Seismic array|seimic arrays]]. The sensors used for a specific problem may vary widely, for example [[microphones|microphone|microphones]], [[Accelerometer|accelerometers]] and [[telescopes|telescope|telescopes]]. However, many simularitiessimilarities 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 spatialrelationships coherence ofbetween the signalsignals received on manyspatially separated sensors can be leveraged for many applications.

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

* determine number and locations of energy-radiating sources

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

* track moving sources

* separation of multiple sources

Array processing metrics are often assesedassessed noisy environments,. thoughThe thismodel for noise may be either one of spatially incoherent noise, or otherone with interfering signals following the same propgationpropagation 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]]