DOA Estimation Using a Greedy Block Coordinate Descent Algorithm
DOA Estimation Using a Greedy Block Coordinate Descent Algorithm
复制标题
使用贪心块坐标下降算法的 DOA 估计
DOI：
10.1109/tsp.2012.2218812
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发表时间：
2012-12-01
影响因子：
5.4
通讯作者：
Ling, Qing
中科院分区：
文献类型：
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作者：
Wei, Xiaohan;Yuan, Yabo;Ling, Qing
针对DOA估计问题，提出了一种新的联合稀疏信号重构算法，与现有的l2，1-范数最小化方法相比，该算法具有更快的收敛速度和更高的估计精度。所提出的贪婪块坐标下降（GBCD）算法与标准的块坐标下降法l2，1-范数最小化相似，但采用了贪婪块选择规则，优先稀疏。虽然贪婪，所提出的算法被证明也具有全局收敛性。通过理论分析，我们证明了它的稳定性，在这个意义上，所有的非零支持所提出的算法是在一定条件下的实际。最后，我们提出了一个加权形式的块选择规则的基础上的MUSIC先验。这种改进大大提高了估计精度，特别是当两个点源相距很近时。数值实验表明，与现有的DOA估计方法相比，该算法具有收敛速度快、重构精度高、抗噪声能力强等优点。
This paper presents a novel jointly sparse signal reconstruction algorithm for the DOA estimation problem, aiming to achieve faster convergence rate and better estimation accuracy compared to existing l2,1-norm minimization approaches. The proposed greedy block coordinate descent (GBCD) algorithm shares similarity with the standard block coordinate descent method for l2,1-norm minimization, but adopts a greedy block selection rule which gives preference to sparsity. Although greedy, the proposed algorithm is proved to also have global convergence in this paper. Through theoretical analysis we demonstrate its stability in the sense that all nonzero supports found by the proposed algorithm are the actual ones under certain conditions. Last, we move forward to propose a weighted form of the block selection rule based on the MUSIC prior. The refinement greatly improves the estimation accuracy especially when two point sources are closely spaced. Numerical experiments show that the proposed GBCD algorithm has several notable advantages over the existing DOA estimation methods, such as fast convergence rate, accurate reconstruction, and noise resistance.