摘要
针对基于L1范数约束的压缩感知理论的恢复算法出现虚假目标,恶化DOA估计性能的问题,提出了一种基于加权L1范数的CS-DOA估计算法.该算法利用噪声子空间与信号子空间的正交性,构造了一个加权矩阵,然后对L1范数约束模型进行加权.通过此加权处理,该算法能够使恢复的系数向量具有更好的稀疏性,并能有效地抑制伪峰,从而获得更精确的DOA估计.仿真结果验证了算法的有效性.
The recovery algorithm of compressive sensing （CS） based on L1 norm constraint may lead to many false targets and deteriorate the performance of DOA estimation. To solve the above problem, a CS-DOA algorithm based on weighted L1 norm was proposed. Using the orthogonality between noise subspace and signal subspace, a weighted matrix was constructed to penalize the L1 norm constrained model. By the weighted processing, the reconstructed coefficient vector with better sparsity could be achieved by using the presented algorithm. What＇ s more, the spurious peaks could also be effectively suppressed. Finally, more accurate DOA estimation could be obtained. Simulation results showed the efficiency of the orooosed method.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第5期654-657,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(60904035)
中央高校基本科研业务费专项资金资助项目(N120423002)
河北省科技厅资助项目(11213502D)
辽宁省自然科学基金资助项目(20102064)
河北省教育厅资助项目(Z2009105)
辽宁省高等学校优秀人才支持计划项目(LJQ2012022)
关键词
波达方向估计
压缩感知
奇异值分解
加权矩阵
L1
范数最小化
DOA estimation
compressive sensing
SVD （singular value decomposition ）
weighted matrix
L1 norm minimization
