基于协方差矩阵重构的单基地MIMO阵列无网格DOA估计方法

提出了一种单基地多输入多输出(multiple-input multiple-output, MIMO)阵列中的协方差矩阵重构的无网格波达方向(direction-of-arrival, DOA)估计方法。该方法通过降维处理将MIMO阵列等效为信噪比(signal-to-noise ratio, SNR)提升的均匀线列阵,将目标方位估计问题转化为混合范数最小化(mixed norm minimization, MixNM)稀疏信号重构问题。进一步给出了与该稀疏重构问题等价的基于网格的凸优化问题,并模型化为半定规划来求解。为了解决网格大小影响估计性能的问题,利用了等价均匀线列阵的托普利兹结构,模型化为半定规划问题来重构无噪声协方差矩阵,最后通过范德蒙分解来估计目标方位。与传统的基于MixNM方位估计方法相比,该方法减少了优化变量个数。与其他离网格方法相比,该方法估计精度不受网格大小的影响,且能够估计相干源目标。实验仿真验证了该方法的有效性。

Gridless DOA estimation method for monostatic MIMO array based on covariance matrix reconstruction

A gridless direction-of-arrival (DOA) estimation method based on covariance matrix reconstruction in monostatic multiple-input multiple-output (MIMO) arrays is proposed. In this method, the MIMO arrays are equivalent to uniform linear arrays with enhanced signal-to-noise ratio (SNR) by the dimension reduction, and the problem of the target azimuth estimation is converted to sparse signal reconstruction based on mixed norm minimization (MixNM). Furthermore, a grid-based convex optimization problem equivalent is presented to the sparse reconstruction problem, which is modeled as a semi-definite programming problem. In order to solve the problem of the performance degradation caused by the grid size, the Toeplitz structure of the equivalent uniform linear array is used to model it into a semi-definite programming problem to reconstruct the noise-free covariance matrix. Finally, the target azimuth is estimated by Vandermonde decomposition. Compared with the traditional method based on the MixNM, the proposed method reduces the number of optimization variables. In addition, compared with other off-grid sparsity representation methods, the accuracy of the proposed method is not affected by the grid size, and it can estimate the coherent source targets. The simulation results show the effectiveness of the proposed method.