Multisensor Automatic Target Classification with Neural Networks

Abstract

This paper presents the multisensor data fusion for airborne target classification with artificial neural network. A feature set, which possesses the dominant characteristics of targets and has a certain dynamic range, is chosen. The entire system consists of identification nets (IN) and classification net (CN). Each identification network is used to extract a particular feature of the target, then the outputs of identification networks are fused by classification network, in which the neural network acts as a decision making processor. In the paper, multilayer perceptrons neural networks trained by back-propagation (BP) rule are discussed. In order to speed up the training or decrease the number of epoch in learning process, both momentum and adaptive learning rate methods are used. The simulation results show that the technique of automatic target classification using neural networks can achieve robust decision performance.