摘要
为了在小样本、低信噪比以及高信源相关性的条件下都能对波达方向(direction of arrival,DOA)进行精确估计,基于压缩感知理论,利用目标信号空间分布的稀疏性,提出了基于加权l_1范数稀疏信号表示的DOA估计算法.该算法对l_1-奇异值分解(singular value decomposition,SVD)算法进行改进,对接收矩阵进行预处理,根据子空间的正交性确定加权矩阵,以加权l_1范数作为最小化的目标函数进行优化得到稀疏信号,进而得到信号的DOA.仿真结果表明,通过加权处理的l_1范数下稀疏信号重构方法能有效抑制偏差,在低信噪比下能够准确稳定地估计出DOA,并且能够提高估计精度.
To accurately estimate the direction of arrival （DOA） under conditions of small samples, low signal-to-noise ratio and high correlations of sources, according to the compressive sensing theory, DOA estimation based on weighted 11 nora1 sparse signal representation was proposed by using the sparse distribution of the spatial signal source bearing. The 11 -SVD algorithm was improved by this algorithm, the receiving matrix was proposed, the weighting matrix was determined according to the orthogonality of the subspace, and the sparse vector was obtained by the optimization using the weighted 1 nora1 as target function for minimization. Then, the DOA of the signal was obtained. Simulations demonstrate that the proposed algorithm can effectively suppress the deviation by sparse signal reconstruction method under weighted processing l1 nora1. At the low SNR, DOA can be estimated accurately and steadily, and the precision of DOA estimation can be improved effectively.
作者
窦慧晶
高立菁
朱子云
DOU Huijing;GAO Lijing;ZHU Ziyun(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2018年第10期1297-1302,共6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)
关键词
稀疏重构
加权矩阵
波达方向
矩阵预处理
凸优化
奇异值分解(SVD)
sparse reconstruction
weighted matrix
direction of mTival
matrix preprocessing
convex optimization
singular value decomposition （SVD）
