基于压缩感知的稀疏重构DOA估计算法  

Sparse Reconstruction DOA Estimation Algorithm Based on Compressive Sensing Theory

作　　者：包晓蕾[1] 曲行根 王卓英[1] 

机构地区：[1]上海电子信息职业技术学院通信与信息工程系,上海201411 [2]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《武汉理工大学学报（信息与管理工程版）》2015年第6期827-831,864,共6页Journal of Wuhan University of Technology：Information & Management Engineering

基　　金：上海市教育委员会教育发展基金资助项目(09CGB06)

摘　　要：基于空间目标分布的稀疏特性和压缩感知理论思想,提出一种基于奇异值分解的多测量梯度投影稀疏重构(SVD-MGPSR)算法,将多目标DOA估计转化为一个稀疏信号重构问题。首先利用阵列流形建立的过完备原子库对信号进行联合稀疏表示,然后对压缩采样后的信息矩阵进行奇异值分解,可以明显降低运算量,最后基于MGPSR算法对稀疏信号进行重构,从而实现DOA估计。相对于已有算法,该算法不仅在低信噪比、小快拍数条件下测向均方误差较小,而且能够对相干信号进行正确估计,具有较高的测向精度和角度分辨率。仿真实验验证了该算法的有效性。Based on the sparse property of the spatial targets distribution and the idea of compressive sensing（ CS） theory,a multi- measurement gradient projection for sparse reconstruction algorithm based on singular value decomposition（ SVD） was proposed,in which a multi- targets DOA estimation problem can be translated into a sparse signal reconstruction problem. Firstly,the signal was joint sparse representation by establishing an over- complete atom dictionary according to array manifold matrix. Then,SVD of information matrix of compressive sampling was done to reduce the amount of computation greatly. Finally,the sparse signal was reconstructed based on multi- measurement vectors gradient projection for sparse signal reconstruction algorithm so as to achieve DOA estimation. Compared with existing algorithm,the proposed algorithm not only has a smaller mean square error in the low SNR but also is able to correctly estimate the coherent signal. Moreover,it offers higher direction finding precision and angular resolution. The simulation results verify its effectiveness.

关 键 词：压缩感知 DOA估计 奇异值分解 梯度投影 

分 类 号：TN911[电子电信—通信与信息系统]