A fuzzy neural-network-model for aspect-independent target identification

A neural network is trained, using the fundamental properties of fuzzy-set theory, to achieve robust aspect-independent radar target identification. The radar cross section of two different aircraft are modeled using a thin-wire-time-domain (TWTD) code to compute their backscattered electric fields for twenty five different aspect angles. The scattered fields corresponding to a few aspect angles are then used to train the network and the rest of the scattered fields are used to test the performance of a neural network for target identification. A fuzzy neural network is found to provide superior performance for target identification compared with both a conventional neural network and a statistical Bayes classifier, especially in a noisy environment.<>