 | This article includes a list of general references, but it lacks sufficient corresponding inline citations. (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. 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.[1][2][3][4]
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.[1]
- ^ a b Torlak, M. Spatial Array Processing. Signal and Image Processing Seminar. University of Texas at Austin.
- ^ J Li, Peter Stoica (Eds) (2009). MIMO Radar Signal Processing. USA: J Wiley&Sons.
- ^ Peter Stoica, R Moses (2005). Spectral Analysis of Signals (PDF). NJ: Prentice Hall.
- ^ J Li, Peter Stoica (Eds) (2006). Robust Adaptive Beamforming. USA: J Wiley&Sons.