基于协方差匹配SLO算法的MIMO雷达DOA估计

利用加权平滑l0范数(Smoothed l0, SL0)算法估计MIMO雷达目标DOA时，需要把协方差矩阵进行矢量化来获得相应的稀疏重构模型，并利用信号和噪声子空间的正交性来构造加权向量。然而当存在相干信源时，MIMO雷达协方差矩阵的秩将退化，这会使得稀疏重构模型的误差较大以及无法正确区分信号和噪声子空间，导致加权SL0算法的DOA估计性能恶化。针对上述问题提出了一种基于协方差匹配SL0算法的MIMO雷达DOA估计方法。该方法利用协方差匹配准则重构出一个满秩的协方差矩阵，恢复MIMO雷达协方差矩阵的Toeplitz特性，并利用协方差逆矩阵的高阶幂来近似噪声子空间从而计算加权向量。仿真分析表明，该方法能够在无需预知信源数目的情况下有效地完成对相干信号的DOA估计。

DOA Estimation of MIMO Radar Based on Covariance Matching SL0 Algorithm

For the DOA estimation with the weighted smoothed l0(SL0) norm algorithm in MIMO radar, the vectorization of covariance matrix is needed to establish a corresponding sparse reconstruction model, and the signal and noise subspaces are used to compute the weight vector. However, due to the coherent sources, the rank of covariance matrix of MIMO radar is degenerated. Therefore, the error of the sparse reconstruction model is large and the signal and noise subspaces cannot be correctly distinguished, which will make the performance of the weighted SL0 algorithm deteriorate. A method based on covariance matching SL0 algorithm for DOA estimation in MIMO radar is proposed. A full rank covariance matrix with the property of Toeplitz is reconstructed by using the covariance matching criteria. The high-order powers of the inverse covariance matrix are used to approximate the noise subspaces for calculating the weighted vector. Experimental results demonstrate that the proposed approach can effectively achieve DOA estimation of coherent signals without knowing the number of sources.