摘要
矩阵填充可以有效恢复阵列信号降采样数据,从而得到等效的全采样回波信号.然而,现有基于矩阵填充的阵列波达方向估计方法要求回波数据在不同快拍下随机选择采样序列,以满足采样数据的随机性.当部分阵元在整个观测时间内关闭或损坏时,上述方法将失效.因此,笔者提出了一种改进的降采样数据恢复方法,利用阵元间的相关特性,将单快拍下的信号矢量变换到一个等效的低秩矩阵,继而通过求解该矩阵的最小核范数,实现对缺失数据的有效估计.仿真结果表明,该方法可以有效恢复降采样数据,抑制噪声,提高波达方向的估计性能.
Matrix Completion （MC） theory can recover the under-sampled data in the array signal processing,further estimating the direction of arrival（DOA）as the fully sampled data does.However,it is required that the data should be under-sampled randomly in different snapshots which satisfy the randomness of MC theory.When some sensors are unsampled or broken in the whole observing time,the previous method would fail.To address this problem,a new processing method is proposed in this paper. The inner relationship among sensors is used,and then we reshape the signal vector in a single snapshort into an equivalent low-rank matrix,which can be recovered effectively by minimizing the nuclear norm. Simulation results validate the effectiveness of the proposed method.Meanwhile,the method can lower the noie power,and improve the performance of the DOA.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2014年第5期30-35,共6页
Journal of Xidian University
基金
国家自然科学基金资助项目(61231017)
国家973计划资助项目(2010CB731903)
关键词
阵列信号
矩阵填充
低秩矩阵
波达方向估计
array signal processing
matrix completion
low rank matrix
direction of arrival
作者简介
杨东（1988-），男，西安电子科技大学博士研究生，E—mail：yangdongxd@gmail．com．
