Direction-of-arrival tracking by parallel array processing
Ana I. Pérez-Neira, Miguel Angel Lagunas · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
Antenna arrays collect multidimensional data that contains signals arriving from different sources. Neural Network Architectures can separate the different signals, thus enabling parallel processing structures. These structures can solve a multi-signal estimation problem more efficiently than the corresponding single signal estimator. Various of these parallel architectures are evaluated in the context of array signal processing. Specifically, the scheme developed in this paper (section 2) uses the spatial diversity supplied by the aperture associated with the sensors to separate the signals and to apply them to a bank of parallel adaptive filters. These filters are then designed in accordance with a mean square error minimization criterion (i.e., a criterion based on Second Order Statistics). As Second Order Statistics assume linearity or Gaussianity they are sometimes overly restrictive. It is shown how High Order Statistics can be very useful when more general criteria such as statistical independence between the signals to be separated are imposed.