Robust adaptive beamforming algorithm based on Sampling Function Neural Network

Adaptive beamforming algorithms that control smart antennas is known to have resolution and interference rejection capability when coefficients of the array factor (AF) is known. However, the performance of adaptive beamforming techniques may degrade severely in the presence of mismatches between the presumed and actual signal steering vectors. This problem can be overcome by neural network approach. In this paper we propose a Sampling Function Neural Network (SFNN) - based robust adaptive beamforming algorithm, which treats the problem of computing the weights of an adaptive array antenna as a mapping problem. This ANN is combination of the Sinc Function in signal processing theory and RBF neural network configuration. This algorithm provides excellent robustness to signal steering vector mismatches, enhances the array system performance under non ideal conditions and makes the mean output array SINR consistently close to the optimal one. Computer simulations results show better performance of our algorithm as compared with existing adaptive beamforming algorithms.