二维GTD模型参数估计的PQ-FB-2D-ESPRIT算法

PQ-FB-2D-ESPRIT algorithm for parameter estimation of 2D-GTD model

二维基于旋转不变技术信号参数估计(2D-estimating signal parameter via rotational invariance techniques,2D-ESPRIT)算法是估计几何绕射理论(geometric theory of diffraction,GTD)模型参数的一种经典算法,但在信噪比较低的条件下,2D-ESPRIT算法的参数估计精度明显下降,噪声鲁棒性较差。针对这一问题,提出一种极化平方前后向平滑2D-ESPRIT(polarized-quadratic-forward-backward 2D-ESPRIT,PQ-FB-2D-ESPRIT)算法,有效地提高了算法的噪声鲁棒性与参数估计性能。改进算法利用目标散射回波数据的极化信息,并通过对协方差矩阵平方处理和前后向空间平滑处理,提高了算法的参数估计性能与数据利用率,同时达到了去相关的效果。仿真结果表明,提出的PQ-FB-2D-ESPRIT算法的参数估计性能及噪声鲁棒性要优于经典2D-ESPRIT算法、前后向平滑2D-ESPRIT(forward-backward 2D-ESPRIT,FB-2D-ESPRIT)算法及平方FB-2D-ESPRIT(quadraticFB-2D-ESPRIT,Q-FB-2D-ESPRIT)算法。基于不同算法估计得到的GTD模型参数对散射中心的定位精度进行比较,进一步验证了改进算法的优越性与有效性。

2D-estimating signal parameter via rotational invariance techniques(2D-ESPRIT)is a classical algorithm to estimate parameters of the two dimensional(2D)geometric theory of diffraction(GTD)model.However,the parameter estimation accuracy and noise robustness of 2D-ESPRIT are poor as signal-to-noise ratio(SNR)decreases.To solve the problem,apolarized-quadratic-forward-backward-2D-ESPRIT(PQ-FB-2D-ESPRIT)algorithm is proposed,which improves noise robustness and parameter estimation performance effectively.The improved algorithm makes full use of the polarization information of radar targets,squares the covariance matrix and performs forward-backward smoothing on the novel covariance matrix.Therefore,the parameter estimation performance and data utilization are improved significantly by this improved algorithm.Simulation experiments verify that the proposed PQ-FB-2D-ESPRIT algorithm has a better noise robustness and more stable parameter estimation performance than the classical 2D-ESPRIT algorithm,forward-backward-2DESPRIT(FB-2D-ESPRIT)algorithm and quadratic-FB-2D-ESPRIT(Q-FB-2D-ESPRIT)algorithm.Based on the GTD model parameters estimated by different algorithms,the positioning accuracies of scattering centers are compared,which can validate the superiorty and effectiveness of the proposed algorithm.