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Khatri-Rao积变换下的离格信号DOA估计
引用本文:李 根,梁玉英.Khatri-Rao积变换下的离格信号DOA估计[J].电讯技术,2017,57(2).
作者姓名:李 根  梁玉英
作者单位:军械工程学院 电子与光学工程系,石家庄,050003
摘    要:当存在离格信号时,基于稀疏表示的波达角(DOA)估计算法性能损失严重.为解决这个问题,在对接收数据协方差矩阵进行Khatri-Rao积变换的基础上,推导了离格信号网格偏离量与紧邻信号原子系数之间的关系,提出了一种单一离格信号DOA估计方法.为提高对邻近离格信号DOA的估计性能,利用矩阵的广义逆性质提出了基于多原子系数的联合估计方法.仿真实验表明,单一离格信号DOA估计方法在低信噪比下有较好的性能,联合估计方法在高信噪比条件下对邻近离格信号DOA有较高的估计精度,同时所提算法估计性能几乎不受网格划分间距的影响,可以通过增大网格间距降低算法运算量.相关研究对阵列天线DOA估计具有一定的参考价值.

关 键 词:离格信号  稀疏表示  DOA估计  Khatri-Rao积变换  压缩感知

Off-grid signal DOA estimation under Khatri-Rao product transform
LI Gen and LIANG Yuying.Off-grid signal DOA estimation under Khatri-Rao product transform[J].Telecommunication Engineering,2017,57(2).
Authors:LI Gen and LIANG Yuying
Abstract:When the off-grid signals appear,the performance of direction-of-arrival( DOA) estimation al―gorithms based on sparse representation suffer serious loss. In order to solve this problem,the Khatri-Rao product transform is performed for covariance matrix of received data,the theoretical relationship between grid deviation and atomic coefficients close to the off-grid signal is derived,and then a method of single off-grid signal DOA estimation is proposed. For improving the DOA estimation performance of off-grid sig―nals with approaching angles, a joint estimation method based on the multiple atomic coefficients is pro―posed by utilizing the generalized inverses properties. The simulation experiments show that the single off-grid signal DOA estimation method has excellent performance under low signal-to-noise ratio( SNR) and the joint estimation method has better performance facing the adjacent off-grid signal under high SNR. At the same time,the grid spacing has few influences on the estimation performance of the proposed algorithm. So enlarging the grid spacing can decrease the calculation amount of the proposed algorithm. These studies have certain reference value for DOA estimation of array antenna.
Keywords:off-grid signal  sparse representation  DOA estimation  Khatri-Rao product transform  com―pressive sensing
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