MIMO Radar Imaging With Nonorthogonal Waveforms Based on Joint-Block Sparse Recovery

Abstract

Multiple-input multiple-output (MIMO) radar imaging is a new technique which may solve the motion compensation problem in inverse synthetic aperture radar (ISAR). However, the imaging result in MIMO radar using matched filtering is usually poor, since the waveforms with the same frequency cannot be fully orthogonal. Sparse signal recovery has the potential to restrain the mutual interference of nonorthogonal waveforms by exploiting the sparsity of targets. However, because the range profile is not as sparse as the 2-D or 3-D image, the sparse recovery result of target range profiles is usually unsatisfactory. In this paper, a joint-block sparsity of range profiles is explored and exploited to improve the range profile quality. And then, the 2-D target image is recovered from the refined range profiles. Furthermore, a robust joint-block sparse recovery algorithm is proposed. The ascent searching direction, the parameter selection method, and the computational complexity of the proposed algorithm are also discussed. Simulation results show that the proposed algorithm is superior to algorithms which just consider sparsity, block sparsity, or joint sparsity. And the quality of the simulated MIMO radar images and real data ISAR images obtained using the new imaging method is better than that of the conventional correlation method and sparse signal recovery method.