Multitarget Localization Based on Sparse Representation for Bistatic MIMO Radar in the Presence of Impulsive Noise

This paper is concerned with the multitarget localization for bistatic MIMO radar in the presence of Symmetric-Stable (SS) impulsive noise. As the non-existence of the second-order matrix degrades the estimation performance of the subspace-based algorithm in SS 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.