Parameter estimation of the GTD model based on performance enhanced MUSIC algorithms
Jiahua Xu, Xiaokuan Zhang, Binfeng Zong, Ninghui Li, Fan Lv, ZhaoLong Wang · 2023
Scattering centers are important features of targets at high frequency regions and the geometric theory of diffraction (GTD) scattering center model is the typical one to describe scattering centers. Therefore, it is quite vital to estimate parameters of GTD model accurately. The classical multiple signal classification (MUSIC) algorithm can effectively estimate these parameters at high signal-to-noise ratios (SNR) but it suffers from poor parameter estimation performance at low SNR scenarios. To solve this problem, we propose a modified MUSIC algorithm which enhances the noise robustness. The modified MUSIC algorithm construct a new total covariance matrix R by averaging the auto-correlation matrix of the original back-scattered data and the auto-correlation matrix of its conjugate data. Then, we take even powers of R ,which can broaden the differences between the eigenvalues of noises and signals and avoid overlapping spectral peaks. The theoretical computational complexities of the main modified step are discussed in this paper. Simulation results verify that the proposed algorithms achieve superior accuracy in parameter estimation of the GTD model and obtain better noise robustness. What is more, a target recognition method based on the GTD model and the artificial intelligence algorithm are proposed in this paper. Simulation results validate the effectiveness of this method.