Classification of Ground Moving Radar Targets with RBF Neural Networks

This paper presents a novel method for classification of targets detected by Ground Moving Target Indication (GMTI) radar systems. GMTI radar systems provide no direct information regarding the type or size of the detected targets. The suggested method allow classification of ground moving targets into few groups of size, by analysis of Signal to Noise Ratio (SNR) values of GMTI radar measurements. The classification method is based on Radial Basis Function (RBF) neural networks. The data used as features for classification composed of Radar Cross Section (RCS) values of the target (obtained from the SNR values) in varying aspect angles. The proposed classifier was tested on diverse simulative cases and yielded very good results in classification of targets for three groups of size.