Adaptive beamforming using complex-valued Radial Basis Function neural networks

Beamforming is an array signal processing problem of forming a beam pattern of an array of sensors. In doing so, beams are directed to the desired direction (beam-pointing) and the nulls are directed to interference direction (null-steering). In this paper, the performance of beamforming using the fully complex-valued RBF network (FC-RBF) with the fully complex-valued activation function is compared with the performance of the existing complex-valued RBF neural networks. It was observed that the FC-RBF network performed better than the other complex-valued RBF networks in suppressing the nulls and steering beams, as desired. The learning speed of the FC-RBF network was also faster than the complex-valued radial basis function network. Comparison of these performances with the optimum matrix method showed that the beampattern of the FC-RBF beamformer was closer to the beampattern of the matrix method.