CS-DOA 估计中观测矩阵性能分析

利用压缩感知(CS)理论解决阵列信号波达方向角(DOA)估计问题,具有对快拍数据量要求低、可处理相关源等优点。CS鄄DOA 估计中的一个关键问题是构建合适的观测矩阵。文中对比分析了均匀线阵与随机稀布阵两种阵列流形的稀疏重构性能,分析结果表明在实际应用中基于随机稀布阵构建的观测矩阵性能更优。仿真实验从三个方面比较了两种观测矩阵的DOA 估计性能,验证了随机稀布阵性能的优越性,在不增加阵元数的前提下,能有效提高阵列的空间角分辨率。

Performance Analysis of Measurement Matrices in CS-DOA Estimation

The method of direction-of-arrival (DOA) estimation of array signals based on compressed sensing (CS) theory has advantages such as fewer snapshots requirement and the capacity of dealing with the coherent sources. One of the key issues of CSDOA estimation is to construct an appropriate measurement matrix. A comparative analysis about the sparse recovery performance of two kinds of array manifold named uniform linear arrays and random thinning arrays is provided in this paper, and the analysis result shows that the performance of measurement matrices constructed by random thinning arrays is better in practical applications. Finally, in the simulation experiments the DOA estimation performance of the two kinds of measurement matrices is compared from three respects, and the advantage of the performance of random thinning arrays is verified that without increasing the number of array elements, random thinning arrays can improve the spatial angular resolution effectively.