基于改进加权空间平滑的凸优化协方差估计

针对相干信号受到非均匀噪声的干扰，在低信噪比环境中常规DOA估计存在估计效果较差甚至失效的情况，基于改进加权空间平滑，提出一种使用凸优化构造最优权重矩阵的方法。改进加权空间平滑算法解相干的同时构造权重矩阵，再用凸优化重构无噪声权重矩阵，将平滑过的协方差矩阵加权，并用MUSIC算法进行DOA估计。仿真结果证实，所提方法相对于空间平滑（spatial smoothing，SS）、基于特征空间MUSIC的空间平滑估计（spatial smoothing and eigen space based MUSIC，SS-ESMUSIC）以及接收信号协方差矩阵秩最小化（spatial smoothing based covariance rank minimization，SS-CRM）算法能更好地抑制非均匀噪声和解相干，且减少了低信噪比的干扰，展现出更优良的分辨力和准确性。

Convex optimization covariance matrix based on modified weighted spatial smoothing algorithm

When processing coherent signals are interfered by non-uniform noise in the cases of low signal-to-noise ratio, the traditional DOA(direction-of-arrival) estimation has poor performance or even failure. To solve the problem, this paper proposed a DOA method based on the modified weighted spatial smoothing method to construct the optimal weight matrix by the convex optimization. The modified weighted spatial smoothing method could deal with extraction of the coherent sources, meanwhile, it constructed the weight matrix. Furthermore, the method reconstructed the noise-free weight matrix by the convex optimization, and weighted the smoothed covariance matrix. Finally, it implemented the DOA by the MUSIC algorithm. The simulation results show that, comparing to the SS-MUSIC, SS-ESMUSIC and SS-CRM algorithm, the method suppresses the non-uniform noise well, solves the coherence more effectively, overcomes the influence of low SNR, and shows better resolution and accuracy.