CLASSIFICATION OF RADAR TARGETS USING SYNTHETIC NEURAL NETWORKS

Radar target classification performance of neural networks is evaluated. Both time-domain and frequency-domain target features are considered. The sensitivity of the neural network algorithm to changes in network topology and training noise level is briefly examined. The problem of classifying radar targets at unknown aspect angles is considered. The performance of the neural network algorithms is compared with decision-theoretic classifiers. It is shown that neural networks can be effectively employed as radar target classification algorithms with an expected performance within 10 dB (worst case) of the optimum classifier.