基于虚拟阵元冗余平均的对称嵌套MIMO雷达DOA估计

针对传统算法进行DOA估计时因删除重复虚拟阵元而造成有效信息损失、估计性能不佳等问题，提出基于虚拟阵元冗余平均的对称嵌套MIMO雷达DOA估计算法。首先，将一组密布均匀线阵和一组稀疏均匀线阵分别以零点为中心对称排列，构成单基地MIMO雷达的发射阵列和接收阵列，将传统的虚拟阵元由“差联合”结构变成对称“和联合”结构形式，提高了系统的自由度、降低了阵元互耦，并将其应用于非相干目标和全相干目标DOA估计；其次，向量化样本协方差矩阵，将“和差联合”阵列重复的虚拟阵元进行冗余平均处理后重构Toeplitz矩阵；最后，结合MUSIC算法进行非相干目标DOA估计，有效提升了目标估计个数和角度估计性能。仿真实验验证了阵列结构和算法的有效性。

Symmetric Nested MIMO Radar DOA Estimation Based on Virtual Array Redundancy Averaging

Aimed at the problems that being caused by deleting the repeated virtual array during DOA estimation, effective information and estimation performance are lost and poor, a symmetric nested MIMO radar DOA estimation algorithm is proposed based on redundancy average of virtual array. Firstly, a set of uniform linear array and a set of sparse uniform linear array are arranged symmetrically by taking zero as a center to constitute a monostatic Multiple Input Multiple Output radar transmitting array and a receiving array. The array structure is to change the conventional virtual array for a symmetric sum coarray structure from the difference coarray structure, thus increasing the degree of freedom, reducing the mutual coupling of the system, and applying it in uncorrelated and coherent targets DOA estimation. Secondly, the vectorized sample covariance matrix and the Toeplitz matrix are reconstructed by averaging the redundant virtual elements of sum difference coarray. Finally, the combination of the proposed algorithm with the MUSIC algorithm for DOA estimation effectively improves the performance of target estimation number and angle estimation. The simulation results show that the proposed array structure and algorithm are effective.