Estimation of Ships’ Complex High-Resolution Range Profiles Based on Sparse Optimization Method in Non-Gaussian Sea Clutter

In high-resolution maritime radar working in scanning mode, the classification and identification of ships require the recovery of the ship's high-resolution range profiles (HRRPs) from radar returns. The return signal from the ship is a complex sparse signal interfered by non-Gaussian sea clutter. In this paper, three sparse optimization methods matching the non-Gaussian characteristics of sea clutter, i.e., the sparse optimization matching K-distribution (SOMK) method, the sparse optimization matching generalized Pareto distribution (SOMGP) method, the sparse optimization matching CGIG distribution (SOMCGIG) method, are proposed to estimate complex HRRPs of ships. The compound Gaussian model is used to describe the non-Gaussianity of sea clutter, and the sparsity of ships' complex HRRPs is constrained by the random distribution with one parameter. In the three methods, the Anderson-Darling (A-D) test is used to search the parameters of the sparse constraint model. Besides, the non-Gaussian characteristics of sea clutter depend on the marine environment parameters and radar operating parameters. For different scenarios, the minimal criterion of the Kolmogorov-Smirnov (K-S) distance is used to select the best model from the three compound Gaussian models, and then select the corresponding proposed methods. Simulated and measured radar data are used to evaluate the performance of the proposed methods and the results show that the proposed methods obtain better estimates of ship HRRPs compared to the recent SRIM method and the classical SLIM method.