冲击噪声背景下基于稀疏表示的双基地MIMO雷达多目标定位

摘要

This paper is concerned with the multitarget localization for bistatic MIMO radar in the presence of Symmetric -Stable ( SS ) impulsive noise. As the non-existence of the second-order matrix degrades the estimation performance of the subspace-based algorithm in SS impulsive noise environment, the preprocessing method is proposed to normalize the received data by maximizing the 2-norm of the row of data. The theoretical analysis proves that the covariance matrix of normalized data is finite. Then the sparse linear model is constructed by performing the vectorization operation on the covariance matrix. And the Covariance Matrix Smoothed L0 norm (CMSL0) method is proposed to estimate the angle of the target. Finally, the Fractional Lower Order Moments (FLOM)-maximum likelihood method is utilized to obtain the location of the target. The simulation results show that both the MUSIC and CMSL0 algorithms can estimate the angle of target effectively after maximizing the 2-norm of the row of received data. The CMSL0 algorithm can obtain better estimation performance and has better robustness against the impulsive noise than the MUSIC algorithm. In addition, compared with the MUSIC algorithm, the CMSL0 algorithm does not require to estimate the actual number of the targets and is not restricted to be within a half wavelength interelement spacing.%该文研究了对称稳定分布( SS )冲击噪声下双基地MIMO雷达的多目标定位问题。针对SS噪声下因二阶矩不存在而造成子空间类算法估计性能下降的不足，提出了矩阵行2范数最大的预处理方法对接收数据进行归一化，使得归一化后的协方差矩阵有界，并以拉直后的协方差矩阵构造稀疏线性模型，提出了基于协方差矩阵-近似零范数(Covariance Matrix Smoothed L0 norm, CMSL0)算法进行目标的发射角和接收角估计。仿真实验表明：通过矩阵行2范数最大化预处理之后，MUSIC(Multiple Signal Classification)和CMSL0算法均能有效地估计出目标的角度，并且CMSL0算法的估计精度及对冲击噪声的稳健性均优于MUSIC算法。此外，与MUSIC算法相比， CMSL0算法不要预先估计目标源的数目，且收发阵元不受半波长间隔的限制。