基于稀疏谱匹配的高分辨DOA估计方法

针对迭代加权最小二乘类稀疏重构算法性能易受过完备基条件数影响的缺陷，提出了一种基于稀疏谱匹配的高分辨DOA估计新方法.对过完备基进行奇异值分解，采用TSVD方法剔除较小奇异值对应的特征向量，获得一个良态矩阵，并用此矩阵替代的过完备基矩阵，采用l_p范数约束正则化FOCUSS算法进行稀疏重构，解决了因网络划分过细造成的过完备基条件数过大带来的病态问题，并用粗、细两步网格划分来降低算法的复杂度.仿真结果表明，相对于MFOCUSS方法，本文方法不仅具有较低的计算复杂度，而且具有更高的分辨率和噪声鲁棒性. 

High Resolution Direction-of-Arrival Estimation Based on Sparse Spectral Fitting

In this paper, a novel high-resolution direction-of-arrival estimation method was presented based on sparse spectral fitting to overcome the drawback that the performance of iterative re-weighted least squares algorithm could be impacted with the overcomplete basis matrix condition number. A singular value decomposition (SVD) was employed to handle the overcomplete basis, adopting the truncated SVD (TSVD) method to remove those singular vectors that corresponded with smaller singular value and obtain a well-conditioned matrix, and using this matrix to replace the overcomplete basis matrix. Then a regularized FOCUSS algorithm with l_p norm constraint was applied for sparse signal reconstruction to resolve ill-posed problem when the overcomplete basis matrix condition number got too large, and coarse-refined space grid separation was used to decrease the computational complexity. Simulation results show that compared with MFOCUSS algorithm, the proposed method can not only reduce computational complexity, but also hold much higher resolution and robustness to noise.