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

In high-resolution maritime radar working in scanning mode, the ship classification and identification need to recover the high-resolution range profile (HRRP) of the ship from radar returns with severe range sidelobe effect. The return signal of a ship in high-resolution maritime radar is a complex sparse signal interfered by non-Gaussian sea clutter. Most of the existing sparse optimization methods assume that the background is Gaussian noise or Gaussian white noise, which is not suitable for ship HRRP estimation in non-Gaussian sea clutter background. In this paper, the compound Gaussian model is used to describe the non-Gaussianity of sea clutter, ship complex HRRPs are modeled by the random sequences of a random distribution with one parameter, and the Bayesian information criterion (BIC) and the minimum K-S distance criterion of the residual and sea clutter model are used to iteratively search the parameters of the sparse constraint model. A sparse optimization method is proposed to match the characteristics of sea clutter: the method of sparse optimization matching K-distribution (SOMK). By comparing the proposed sparse optimization method with the classical sparse learning via iterative minimization (SLIM) method on radar simulation data, it indicates that the proposed sparse optimization method has better performance for ship HRRP estimation.