Automatic target recognition using modularly cascaded vector quantizers and multilayer perceptrons

Abstract

An automatic target recognition classifier is constructed of a set of vector quantizers (VQs) and multilayer perceptrons (MLPs) that are modularly cascaded. A dedicated VQ codebook is constructed for each target at a specific range of aspects. Each codebook is a set of block feature templates that are iteratively adapted to represent a particular target at a specific range of aspects. These templates are further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory power. The mean squared errors resulting from matching the input image with the block templates in each each codebook are input to an array of window MLPs (WMLPs). Each WMLP is trained to recognize its intended-target at a specific range of aspects. The outputs of the WMLPs are manipulated and fed into a target MLP (TMLP) that produces the final recognition results. A recognition rate of 65.3 percent is achieved on a highly cluttered test set.