基于改进稀疏度自适应匹配追踪算法的压缩感知DOA估计  

Compressed Sensing DOA Estimation Based on Improved Sparsity Adaptive Matching Pursuit Algorithm

作　　者：窦慧晶 肖子恒 杨帆 DOU Huijing;XIAO Ziheng;YANG Fan(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124

出　　处：《北京工业大学学报》2021年第11期1239-1246,共8页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61171137);北京市教育委员会科研发展计划资助项目(KM201210005001)。

摘　　要：由于传统子空间类算法在少快拍数、低信噪比(signal noise ratio,SNR)、信源相干等条件下波达方向(direction of arrival,DOA)估计精度低甚至无法估计,因此,研究压缩感知理论在DOA估计中的应用.针对稀疏度自适应匹配追踪算法在噪声环境下无法有效估计以及选择大的初始步长会导致过估计的问题,在该算法的基础上进行改进,利用迭代残差的变化规律优化算法的迭代终止条件,同时通过对步长大小进行自适应调整来快速准确逼近信源稀疏度.仿真结果表明,所提算法具有估计精度高、运行速度快、对噪声有较好的鲁棒性等优势,促进实际情况下压缩感知与DOA估计的进一步融合.Because traditional subspace algorithms have low direction of arrival(DOA)estimation accuracy or cannot be estimated under the conditions of few snapshots,low signal-to-noise ratio,and source coherence,the application of compressed sensing theory in DOA estimation was studied in this paper to solve the problem that sparsity adaptive matching pursuit algorithm cannot be effectively estimated in a noisy environment and the selection of a large initial step causes overestimation.On the basis of this algorithm,the iteration termination conditions of the algorithm were improved and optimized by using the variation rule of the iteration residuals.The adaptive adjustment of the step size can quickly and accurately approach the source sparsity.The simulation results show that the proposed algorithm has the advantages of high estimation accuracy,fast running speed,and good robustness to noise.It promotes the further fusion of compressed sensing and DOA estimation under actual conditions.

关 键 词：波达方向估计 压缩感知 稀疏度自适应 残差 步长 迭代终止条件 

分 类 号：TN911[电子电信—通信与信息系统]