Low-angle radar tracking using radial basis function neural network

The authors apply the radial basis function (RBF) neural network to low-angle radar tracking. Computer simulations show that the RBF network is capable of tracking both stationary and moving targets with high accuracy. Also, the tracking performance of the RBF network is evaluated under different signal-to-noise ratio situations. Real-life data are used to test the RBF network. The results demonstrate the robustness and effectiveness of the network in terms of its independence of array errors and of the nature of the noise background