Array 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 seimic 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. S XX ( f ) = ∫ − ∞ ∞ R XX ( τ ) cos ⁡ ( 2 π f τ ) , d τ {displaystyle S_{ ext{XX}}(f)=int _{-infty }^{infty }R_{ ext{XX}}( au )cos(2pi f au ),mathrm {d} au }     (I) R XX ( τ ) = ( V X ( t ) V X ( t + τ ) ) {displaystyle R_{ ext{XX}}( au )=left(V_{X}(t)V_{X}(t+ au ) ight)}     (II) 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 seimic 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.