基于改进3D-ESPRIT算法的GTD模型参数估计与目标识别

针对低信噪比条件下，传统的基于旋转不变技术的三维信号参数估计(three-dimensional estimating signal parameter via rotational invariance te chniques, 3D-ESPRIT)算法和平方前后向平滑的3D-ESPRIT(quadr atic-forward-backward 3D-ESPRIT, Q-FB-3D-ESPRIT)算法对几何绕射理论(geometric theory of diffraction, GTD)模型参数估计精度显著降低的问题，提出改进的极化Q-FB-3D-ESPRIT (polarized-Q-FB-3D-ESPRIT, PQ-FB-3D-ESPRIT)算法。改进算法与上述两种传统算法相比，增加了对目标极化信息的利用，有效延长了可利用电磁散射数据的长度。仿真结果表明，改进算法的参数估计精度要高于其他两种算法，且在低信噪比情况下尤为显著。此外，还对基于散射中心模型的雷达目标识别进行了研究，仿真结果进一步验证了所提算法的可行性。

GTD model parameter estimation and target recognition based on improved 3D-ESPRIT algorithm

Aiming at the problem that the traditional three-dimensional estimating signal parameter via rotational invariance techniques(3D-ESPRIT)algorithm and quadratic-forward-backward 3D-ESPRIT(Q-FB-3D-ESPRIT)algorithm significantly reduce the estimation accuracy of geometric theory of diffraction(GTD)model parameters under the condition of low signal to noise ratio(SNR),an improved polarized-Q-FB-3D-ESPRIT(PQ-FB-3D-ESPRIT)algorithm is proposed.Compared with the above two traditional algorithms,the improved algorithm increases the use of target polarization information and effectively extends the length of available electromagnetic scattering data.The simulation results show that the parameter estimation accuracy of the improved algorithm is higher than that of the other two algorithms,especially in the case of low SNR.In addition,the radar target recognition based on scattering center model is studied,and the simulation results further verify the feasibility of the proposed algorithm.