CLASSIFICATION OF RADAR CLUTTER USING NEURAL NETWORKS

We have constructed a neural network classifier to successfully distinguish between several major classes of radar returns including weather, birds, and aircraft. This classifier incorporates both preprocessing and postprocessing procedures, as well as a multilayer feedforward network (based on the back-propagation algorithm) in its design. It achieves an average classification accuracy of 89% on generalization for data collected during a single scan of the radar antenna. In this paper, we describe the procedures of feature selection for neural network training, the classifier design considerations, the learning algorithm development, the implementation, and the experimental results of the neural clutter classifier, which is simulated on a Warp systolic computer. The paper also includes a comparative evaluation of the multilayer neural network with a traditional Bayes classifier.