GTD Model Parameters Estimation and RCS Reconstruction Based on the Improved LS-ESPRIT Algorithm

Abstract

The traditional Least Squares-Estimating Signal Parameter via Rotational Invariance Techniques (LS-ESPRIT) algorithm is not effective while estimating parameters of the Geometric Theory of Diffraction (GTD) at lower SNR. To solve this problem, an improved LS-ESPRIT algorithm is proposed in this paper. Firstly, a Hankel matrix is constructed by the echo data of radar targets.Secondly,a low- rank reconstructed Hankel matrix is obtained,which is solved by the nuclear norm convex optimization method. Finally, the traditional LS-ESPRIT algorithm is used to process the data after noise reduction and estimate the parameters of the GTD model. Moreover,the reconstructed Radar Cross Section (RCS) can be obtained by the traditional LS-ESPRIT algorithm and the improved LS-ESPRIT algorithm. The influence of different bandwidths on parameter estimation is also analyzed in this paper. Simulation results show that the estimation accuracy and noise resistance of the improved LS-ESPRIT algorithm is better than the traditional LS-ESPRIT algorithm and the traditional TLS-ESPRIT algorithm. Furthermore, the amplitude error and phase angle error of the RCS which is reconstructed by the improved algorithm are smaller than the traditional algorithm. Different bandwidths also have influences on parameter estimation accuracy, the more wider bandwidth is, the more accurate parameters can be estimated.