Reconstructed target range profile via unitary ESPRIT superresolution algorithm

Abstract

We propose an efficient technique for target classification using one-dimensional high resolution range profile (HRRP). The proposed technique utilizes the unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm to extract scattering centers and then reconstruct superresolution range profiles. Moreover, we employ the central moments to provide translation invariant and scale invariant feature vectors. Finally, the proposed unitary ESPRIT (U-ESPRIT) based range profile reconstruction method is applied to the simulated annealing resilient backpropagation (SARPROP) classification algorithm to evaluate the recognition performances. Recognition results using four different aircraft models are presented to assess the effectiveness of the proposed technique, and they are compared with those of the conventional range profiles obtained by fast Fourier transform (FFT). Comparison results on simulated data show that the HRRP reconstruction method is better than directly using HRRP in targets classification.