Parameter estimation of the 2D-GTD model and RCS reconstruction based on an improved 2D-ESPRIT algorithm

摘要

The two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm is a classical method to estimate parameters of the two-dimensional geometric theory of diffraction (2D-GTD) model. While as signal-to-noise-ratio (SNR) decreases, the parameter estimation performance of 2D-ESPRIT algorithm is severely influenced. To solve this problem, a performance-enhanced 2D-ESPRIT algorithm is proposed in this article. The improved 2D-ESPRIT algorithm combines the conjugate data with the original back-scattered data and obtains a novel covariance matrix by squaring the original total covariance matrix. Simulation results indicate that the improved algorithm has a better noise robustness and a more stable parameter estimation performance than the classical ESPRIT algorithm and the classical TLS-2D-ESPRIT algorithm. To further validate the superiority of the improved 2D-ESPRIT algorithm, reconstructed radar cross section (RCS) is presented in this article. Compared with the classical 2D-ESPRIT algorithm, the proposed algorithm presents higher RCS fitting precision. Furthermore, the impacts of other factors on parameter estimation, such as matrix pencil parameters and paring parameters, are also studied in this article.