摘要
为了提高脉冲噪声环境下基于二阶协方差矩阵差分(COV-MD)的远近场混合源定位算法的估计性能,本文提出了基于分数低阶协方差矩阵差分(FLOC-MD)和基于压缩变换协方差矩阵差分(CTC-MD)的远近场混合源定位算法。所提出的算法首先利用一维MUSIC谱峰搜索获得远场源信号的方位角估计,然后利用矩阵差分法实现远近场信号源的分离得到扩展的近场源分数低阶协方差矩阵(或压缩变换协方差矩阵),最后在利用类旋转不变方法(ESPRIT-Like)估计得到的近场源方位角的基础上,再次利用一维MUSIC谱峰搜索获得近场源距离的估计。计算机仿真结果表明:CTC-MD算法和FLOC-MD算法在强脉冲和低信噪比情况下的估计性能都要明显优于COV-MD算法和其他基于二阶统计量的远近场混合源定位算法,同时CTC-MD算法的性能要好于FLOC-MD算法并且不依赖于脉冲噪声的先验信息。
In order to improve the performance of mixed far and near field sources location algorithm based on matrix differ⁃encing of the second-order covariance matrix(COV-MD)under impulsive noise environment,two revised algorithm based on matrix differencing of the fractional low-order covariance matrix(FLOC-MD)and compressed transform covariance ma⁃trix(CTC-MD)are proposed in this paper.Firstly,the proposed algorithms use one-dimensional MUSIC spectral peak search to obtain the direction of arrival(DOA)of far field source signal.Then the matrix difference algorithm is used to eliminate the far field components and reconstruct the extend near field components.Finally,based on the DOA of nearfield source which is estimated by the ESPRIT-like method,the range of near field source is estimated by another onedimensional MUSIC spectral peak search.Computer simulation results show that the performance of CTC-MD algorithm which is not depend on the prior information of the impulsive noise is better than FLOC-MD algorithm,and the performance of CTC-MD and FLOC-MD algorithm are both better than that of COV-MD algorithm and other mixed far and near field sources location algorithms based on second-order statistics in the case of strong impulsiveness and lower signal-to-noise ratio.
作者
母采凤
李森
吕梦然
MU Caifeng;LI Sen;LYU Mengran(Information Science and Technology College,Dalian Maritime University,Dalian,Liaoning 116026,China)
出处
《信号处理》
CSCD
北大核心
2022年第11期2342-2349,共8页
Journal of Signal Processing
基金
国家自然科学基金面上项目(61971083)
中央高校基本科研业务费资助项目(3132019341)。
关键词
远近场混合源
脉冲噪声
矩阵差分
分数低阶协方差
压缩变换协方差
mixed far and near field sources
impulsive noise
matrix differencing
fractional lower order covariance
compression transform covariance
