摘要
针对现有迭代收缩算法在低信噪比、小快拍、多信源条件下阵列信号DOA估计中存在的不足.提出一种基于迭代重加权最小二乘算法的矢量水听器阵列信号DOA估计方法,该方法在压缩感知理论的基础上对空域网格进行稀疏划分,通过构造超完备字典集,利用迭代收缩步骤对所得稀疏解进行收缩操作,进而重构出多快拍模型下的稀疏信号.然后对重构得到的信号矩阵取行的l_(2)-范数,根据稀疏度选取行的l_(2)-范数最大值并将其映射到对应的空域网格划分间隔即可得到DOA的估计值.仿真实验结果表明,该算法在低信噪比、小快拍、多信源的情形下比现有的一些迭代收缩算法具有更高的成功率,不受相干信源的影响,而且具有较强的鲁棒性.
Addressing the shortcomings of existing iterative shrinkage algorithms in DOA estimation for array signals under conditions of low SNR,small snapshot numbers,and multiple sources,a DOA estimation method for vector hydrophone array signals based on the Iterative Reweighted Least Squares algorithm is proposed.This method performs sparse division of the spatial grid based on Compressed Sensing theory.By constructing an overcomplete dictionary set,it employs iterative shrinkage steps to shrink the obtained sparse solution,thereby reconstructing the sparse signal under a multi-snapshot model.Subsequently,the L2-norm of the rows of the reconstructed signal matrix is taken,and the maximum L2-norm value of the rows is selected based on sparsity,which is then mapped to the corresponding spatial grid division intervals to obtain the DOA estimates.Simulation results demonstrate that this algorithm achieves a higher success rate than some existing iterative shrinkage algorithms in scenarios of low SNR,small snapshot numbers,and multiple sources.It is unaffected by coherent sources and exhibits strong robustness.
作者
王立府
禹秀梅
乔淑慧
王鹏
WANG Li-fu;YU Xiu-mei;QIAO Shu-hui;WANG Peng(School of Mathematics,North University of China,Taiyuan 030051,China)
出处
《数学的实践与认识》
北大核心
2025年第7期131-144,共14页
Mathematics in Practice and Theory
基金
山西省留学回国人员科技活动择优资助项目(20240011)
山西省基础研究计划资助项目(202103021224212)
山西省回国留学人员科研项目(2021-108)。
