A new neural network approach to the target tracking problem with smart structure

The algorithm presented in this paper, namely the modified neural multiple source tracking algorithm (MN-MUST) is the modified form of the recently published work, a NN algorithm, the neural multiple-source tracking (N-MUST) algorithm, was presented for locating and tracking angles of arrival from multiple sources. MN-MUST algorithm consists of three stages that are classified as the detection, filtering and DoA estimation stages. In the first stage a number of radial basis function neural networks (RBFNN) are trained for detection of the angular sectors which have source or sources. A spatial filter stage applied individually to the every angular sector which is classified in the first stage as having source or sources. Each individual spatial filter is designed to filter out the signals coming from all the other angular sectors outside the particular source detected angular sector. This stage considerably improves the performance of the algorithm in the case where more than one angular sector have source or sources at the same time. Insertion of this spatial filtering stage is the main contribution of this paper. The third stage consists of a neural network trained for DoA estimation. In all three stages neural network´s size and the training data are considerably reduced as compared to the previous approach, without loss of accuracy