An identification method using ESPRIT and PCA for radar targets in resonance region

This paper aims at the development of a fast and sufficiently aspect-independent identification method with the processing of wideband scattered signals from radar targets. In the given method, estimation of signal parameters by rotational invariance techniques (ESPRIT) is mainly used for the purpose of classification of targets in resonance region. Multiple reference vectors acquired by using this technique are reduced to one vector for each target with principal component analysis (PCA) and this sole vector is used as feature vector of the given target. The identification phase is employed according to the highest correlation coefficients between ESPRIT vectors of test signals and feature vectors. The proposed method is applied to small-scale airplane targets modeled with wires and about 88 percent identification rate is obtained even for SNR level of -5 dB. The results for other similar dimension reduction techniques are also demonstrated, and PCA is found to give better accuracy rates.