DoA estimation of highly correlated stochastic sources using neural model

MultiLayer perceptron (MLP)-based neural model for an efficient 1D DoA estimation of highly correlated stochastic EM sources is proposed. Model extracts this information from a spatial correlation matrix obtained by sampling stochastic signals by a receiving antenna array. Algorithm for optimal selection of correlation matrix elements from the upper triangle of the matrix is suggested to reduce the number of antenna array elements needed to obtain an accurate neural model with relatively simple architecture. This approach is verified by determining the azimuth position of two sources with the mutual correlation in the range [0.8–0.95] using antenna arrays with different number of elements.