Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China.
Sensors (Basel). 2013 Aug 22;13(9):11167-83. doi: 10.3390/s130911167.
DOA (Direction of Arrival) estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property) condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering vectors corresponding to different DOAs. Thus, it violates the mutual coherence condition. The situation gets worse when there are two sources from two adjacent DOAs. In this paper, an algorithm based on OMP (Orthogonal Matching Pursuit), called ILS-OMP (Iterative Local Searching-Orthogonal Matching Pursuit), is proposed to improve DOA resolution by Iterative Local Searching. Firstly, the conventional OMP algorithm is used to obtain initial estimated DOAs. Then, in each iteration, a local searching process for every estimated DOA is utilized to find a new DOA in a given DOA set to further decrease the residual. Additionally, the estimated DOAs are updated by substituting the initial DOA with the new one. The simulation results demonstrate the advantages of the proposed algorithm.
到达方向（DOA）估计是阵列信号处理应用中的一个主要问题。最近，压缩感知算法，包括凸松弛算法和贪婪算法，已被认为是一种新颖的 DOA 估计算法。然而，这些算法的成功受到 RIP（限制等距性质）条件或测量矩阵的互相干性的限制。在 DOA 估计问题中，测量矩阵的列是对应于不同 DOA 的导向矢量。因此，它违反了互相干条件。当来自两个相邻 DOA 的两个源时，情况会变得更糟。在本文中，提出了一种基于 OMP（正交匹配追踪）的算法，称为 ILS-OMP（迭代局部搜索-正交匹配追踪），通过迭代局部搜索来提高 DOA 分辨率。首先，使用传统的 OMP 算法获得初始估计 DOA。然后，在每次迭代中，针对每个估计 DOA 进行局部搜索过程，以在给定的 DOA 集中找到新的 DOA，进一步减小残差。此外，通过用新的 DOA 替换初始 DOA 来更新估计的 DOA。仿真结果证明了所提出算法的优势。
