Neural Solutions for High Range Resolution Radar Classification

Abstract

In this paper the application of neural networks to Automatic Target Recognition (ATR) using a High Range Resolution radar is studied. Both Multi-layer Perceptrons (MLP) and Radial Basis Function Networks (RBFN) have been used. RBFNs can achieve very good results with a considerably small size of the training set, but they require a high number of radial basis functions to implement the classifier rule. MLPs need a high number of training patterns to achieve good results but when the training set size is higher enough, the performance of the MLP-based classifier approaches the results obtained with RBFNs, but with lower computational complexity. Taking into consideration the complexity of the HRR radar data, the choice between these two kind of neural networks is not easy. The computational capability and the available data set size should be considered in order to choose the best architecture. MLPs must be considered when a low computational complexity is required, and when a large training set is available; RBFNs must be used when the computational complexity is not constrained, or when only few data patterns are available.