基于空间角稀疏表示的二维DOA估计

Two Dimensional DOA Estimation Based on Sparse Representation of Space Angle

论文提出一种基于空间角稀疏表示的2维DOA估计方法,解决了2维DOA估计中冗余字典构造的难题。构建的空间角包含了方位角和俯仰角的信息,利用其构造冗余字典可以将方位角和俯仰角的组合从2维空间映射到1维空间,极大地降低了字典的长度和求解的复杂度;同时将算法推广到频域,扩展了其应用范围。与传统的高分辨算法相比,该方法对信噪比和快拍数要求不高、无需特征值分解和多维搜索过程。理论分析和仿真实验,验证了该方法对2维相干信号和非相干信号都具有较高的估计精度和较好的分辨力,在不同信噪比下性能优于MUSIC算法。

To solve the problem of estimating two dimensional Direction Of Arrival （DOA） using sparse representation, a novel DOA estimation method is proposed to estimate the DOAs of two-dimensional signals based on sparse representation of space angle. So the space angle is put forward to construct the dictionary. By this way, the dimension of dictionary is reduced to one-dimension from two-dimensional space, and the length of the redundant dictionary is largely reduced. Then the algorithm is extended to frequency domain, and the frequency sparse representation of space angle method is presented. Compared with the traditional high-resolution methods, the proposed method has lower SNR threshold and smaller snaps. Theoretical analysis and simulation experiments verify the algorithm has a better performance in the aspect of precision and resolution to estimate the DOAs of two-dimensional coherent signals than the MUSIC algorithm.