基于稀疏表示的多输入多输出雷达多目标定位

Multi-target localization for MIMO radar based on sparse representation

针对双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达目标定位问题,提出一种基于稀疏表示的双基地MIMO雷达多目标定位方法.利用点目标所在的二维角度空间构造冗余字典;通过对接收信号的协方差矩阵进行特征分解,从中选取不同数目的特征向量在该冗余字典下稀疏表示,构建以特征向量为观测信号的多重测量向量(Multiple Measurement Vectors,MMV)模型,提取的特征向量在充分包含目标的角度信息的前提下,降低了直接以接收信号为观测信号的矩阵维数,形成低维稀疏线性模型;最后,通过特征向量的稀疏重构,得到目标的角度估计.与现有算法相比,该算法对特征向量的稀疏重构降低了重构原始接受信号的计算复杂度,且在低信噪比和低快拍下仍有较好的估计性能,仿真实验验证了算法的有效性.

A new method is proposed for the multi-target localization of bistatic multiple-input multiple-output（MIMO）radar based on the sparse representation.Firstly,a redundant dictionary is built based on the two-dimensional scene where the targets locate.Then,agiven number of eigenvectors as observation signals obtained from the covariance matrix of array received signals are sparsely denoted in the redundant dictionary,and constructed a multiple-measurement vectors（MMV）model,namely a low-dimensional sparse linear model which reduces the matrix dimension directly using the received signals as observation signals under the premise of containing angle information of the targets.Finally,angle estimation is obtained by the sparse recovery algorithm.Compared with the existing algorithm,the proposed algorithm reduces the computational complexity of directly reconstructing the original signals and performs well even under low SNR and low snapshots.The simulation results verify that the proposed method is effective.