An efficient method for estimating complex high-resolution range profiles of ships

Abstract

High-resolution range profiles (HRRPs) of ships are significant for ship classification and recognition, sparse recovery methods are a major tool to attain ship HRRPs from high-resolution radar echoes. Aiming at the problem that complex HRRPs of ships are not finely modeled in the sparse recovery via iterative minimization (SRIM) method, this paper proposes a new SRIM method to estimate complex HRRPs of ships from radar echoes corrupted by non-Gaussian sea clutter. In the new SRIM method, ship HRRPs are modeled by a bi-parametric lognormal distribution (LND), and high-resolution sea clutter is characterized by the compound-Gaussian distribution with inverse Gaussian texture (CG-IG). The new SRIM method is compared with the classic sparse learning via iterative minimization (SLIM) method, the linear programming-based (LP-based) method, and the recent SRIM method by adopting simulated and measured radar data, the experimental results show that the new SRIM method effectively reduces the central processing unit (CPU) time of ship HRRP estimation while attaining better performance than the existing methods.