Vehicles classification using Z-score and modelling neural network for forward scattering radar

This paper presents the improvement of vehicle classification in forward scattering radar (FSR) using a new classification technique. The technique is a combination between two methods which are Z-score and neural network (NN). The Z-score is used to extract the features of target signature while neural network is used as a classifier to classify the size of vehicles. The results of vehicle classification for three different frequencies in this case 64, 151 and 434 MHz are presented and discussed. The results reveal that the proposed technique is effective to perform ground target classification.