Radar target classification method with high accuracy and decision speed performance using MUSIC spectrum vectors and PCA projection

Abstract

This paper introduces the performance of an electromagnetic target recognition method in resonance scattering region which includes pseudo spectrum Multiple Signal Classification (MUSIC) algorithm and principal component analysis (PCA) technique. The aim of this method is to classify an unknown target as one of the known targets in an aspect-independent manner. The suggested method initially collects the late-time portion of noise-free time-scattered signals obtained from different reference aspect angles of known targets. Afterward these signals are used to obtain MUSIC spectrums in real frequency domain having super-resolution ability and noise resistant feature. In the final step PCA technique is applied to these spectrums in order to reduce dimensionality and obtain only one feature vector per known target. In the decision stage noise-free or noisy scattered signal of an unknown (test) target from an unknown aspect angle is initially obtained. Subsequently MUSIC algorithm is processed for this test signal and resulting test vector is compared with feature vectors of known targets one by one. Finally the highest correlation gives the type of test target. The method is applied to wire models of airplane targets and it is shown that it can tolerate considerable noise levels although it has a few different reference aspect angles. Besides the runtime of the method for a test target is sufficiently low which makes the method suitable for real-time applications.