Comparison of two neural net classifiers to a quadratic classifier for millimeter-wave radar

This paper describes the comparison of three classifiers for use in an automatic target recognition (ATR) system for millimeter wave (MMW) radar data. The three classifiers were the quadratic (Bayesian-like), the multilayer perceptron using a backpropagation training algorithm (termed backpropagation for short), and the counterpropagation network. Two data sets, statistical with four classes and real radar data with three classes, were used for training and testing all three classifiers. Three experiments were performed including: comparing the performances between the three classifiers on both the statistical feature set and the real radar data; optimal configuration for the backpropagation network; and the number of training iterations required for optimal performance using the backpropagation network before overtraining occurred.