Source localization in a multipath environment via beamspace cumulant-based neural processing

An application of the radial-basis function neural networks (RBFNN) to the angle-of-arrival (AOA) estimation of a desired source in multipath environments is investigated. In conjunction with a set of judiciously constructed beamformers, the RBFNN are used to estimate the desired AOA within an angular sector of interest (ASOI). With a pilot signal emitted from each of the training AOAs within the ASOI, the RBFNN is trained with the higher-order statistics (HOS) estimated from the received array data. In principle, the RBFNN AOA estimator maps the complex HOS into the desired angle response as an function approximator. By matching the HOS to the center vectors associated with the hidden nodes and linearly combining the node values, an AOA estimate results. The efficacy of the proposed AOA estimator is confirmed by computer simulations