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Neural network-based smart antennas are used for the solution of multiple-source tracking problems in the area of wireless communications. The architecture of the neural network is constructed in two stages, one stage for signal detection and the other for angle of arrival (AOA) estimation. The best candidates for this type of problem are radial basis function neural networks (RBFNN), applied in both stages. Progress is made by applying probabilistic neural networks (PNN) in the first stage. This rapidly reduces the time for network training. Simulation results are performed to investigate the performance of the algorithm.