Sparse Recovery via Iterative Minimization for the Recovery of Complex-Valued Target High-Resolution Range Profile

Compressed sensing (CS) is a technique that allows the recovery of sparse signal from fewer observations than that required by the traditional Nyquist criterion. It has been applied usually by considering the recovery of sparse signal from the real-valued or complex-valued observations contaminated by Gaussian noise. However, observations corrupted by non-Gaussian clutter are very common in practice, such as maritime surveillance radars for radar automatic target recognition (RARC). High-resolution range profile (HRRP) is paid intensive attention by the radar automatic target recognition community. Therefore, recovery of complex-valued target HRRP from complex-valued radar data with sea clutter of strong non-Gaussianity is an important task. In this paper, a novel sparse recovery via iterative lpnorm (0<; p≤l) minimization (SRIM) method is proposed to deal with the case. The results of simulated experiment show that the SRIM method obtains better recovery precision of HRRP from radar returns corrupted by sea clutter of strong nonGaussianity.