互耦条件下基于稀疏重构的MIMO雷达角度估计  

Sparse Reconstruction-based Direction of Arrival Estimation for MIMO Radar in the Presence of Unknown Mutual Coupling

作　　者：肖炯 唐波[1] 王海 XIAO Jiong;TANG Bo;WANG Hai(College of Electronic Engineering,National University of Defense Technology,Hefei 230037,China)

机构地区：[1]国防科技大学电子对抗学院,合肥230037

出　　处：《雷达学报（中英文）》2024年第5期1123-1133,共11页Journal of Radars

基　　金：国家自然科学基金(62171450);安徽省杰出青年科学基金(2108085J30);国防科技大学自主创新科学基金(23-ZZCX-JDZ-42)。

摘　　要：为了降低阵列互耦对多输入多输出(MIMO)雷达波达角度(DOA)估计性能的影响,实现少量快拍条件下的目标角度估计,该文提出了基于迭代最小化稀疏学习(SLIM)算法的互耦校正和目标角度估计算法。所提算法利用目标回波信号的空域稀疏性,通过迭代优化算法估计了MIMO雷达发射和接收阵列的阵元互耦系数,以及目标稀疏空间谱。该算法无需设置超参数,且具有良好的收敛特性。仿真结果表明,当MIMO雷达发射和接收阵列存在互耦时,如果目标角度间隔较小,所提算法能够在较高信噪比条件下基于少量快拍高精度地估计目标角度;如果目标角度间隔较大,则在较低信噪比和少量快拍条件下仍有较高的角度估计精度。To improve the accuracy of Direction Of Arrival(DOA)estimation in Multiple Input Multiple Output(MIMO)radar systems under unknown mutual coupling,we propose a mutual coupling calibration and DOA estimation algorithm based on Sparse Learning via Iterative Minimization(SLIM).The proposed algorithm utilizes the spatial sparsity of target signals and estimates the spatial pseudo-spectra and the mutual coupling matrices of MIMO arrays through cyclic optimization.Moreover,it is hyperparameter-free and guarantees convergence.Numerical examples demonstrate that for MIMO radar systems under unknown mutual coupling conditions,the proposed algorithm can accurately estimate the DOA of targets with small angle separations and relatively high Signal-to-Noise Ratios(SNRs),even with a limited number of samples.In addition,low DOA estimation errors are achieved for targets with large angle separations and small sample sizes,even under low-SNR conditions.

关 键 词：MIMO雷达 波达角度估计 阵列互耦 迭代最小化稀疏学习算法 少量快拍 

分 类 号：TN959.1[电子电信—信号与信息处理]