Neural networks for narrowband and wideband direction finding

The authors have investigated the narrowband direction-finding problem with neural networks, and propose taking into consideration several snapshots of array data when programming the network. This yields an average version of the interconnection strengths and bias terms that program the network. Simulation results show that using these averaged interconnection strengths and bias terms enhances the performance of the network compared to the method used by R. Rastogi et al. (1987). The authors extend the direction-finding problem to the wideband case in which the sources have broad temporal frequency spectrum. They utilize the information present at different frequencies in the source spectrum to obtain a suitable cost function that can be minimized by a neural network