Analytical derivation of distortion constraints and their verification in a learning vector quantization-based target recognition system

Abstract

We obtain a novel analytical derivation for distortion-related constraints in a neural network- (NN)-based automatic target recognition (ATR) system. We obtain two types of constraints for a realistic ATR system implementation involving 4-f correlator architecture. The first constraint determines the relative size between the input objects and input correlation filters. The second constraint dictates the limits on amount of rotation, translation, and scale of input objects for system implementation. We exploit these constraints in recognition of targets varying in rotation, translation, scale, occlusion, and the combination of all of these distortions using a learning vector quantization (LVQ) NN. We present the simulation verification of the constraints using both the gray-scale images and Defense Advanced Research Projects Agency's (DARPA's) Moving and Stationary Target Recognition (MSTAR) synthetic aperture radar (SAR) images with different depression and pose angles.