Fast Estimation of Complex High-Resolution Range Profiles of Ships via Amplitude–Position Bi-Iterative Sparse Recovery Algorithm

Abstract

Radar high-resolution range profiles (HRRPs) of ships are important in ship classification and recognition. Sparse recovery algorithms are a major tool for acquiring HRRPs from radar returns. Statistical models of ship HRRPs and sea clutter form the foundation to develop effective and efficient algorithms. In this article, ship HRRPs are modeled using biparametric lognormal distributions with heavy tails and high sparsity. Sea clutter is modeled using a compound-Gaussian model with inverse Gaussian texture (CGIG) distributions. Based on the two models, a fast sparse recovery algorithm, named the Amplitude–Position Bi-iterative Sparse Recovery Algorithm, is proposed to estimate ship HRRPs. In addition to sparsity along range cells, ship HRRPs exhibit nongrid structure, and ship scatterers are frequently not located at the centers of range cells, resulting in microposition offsets. The range-oversampled model can handle nongrid structures but requires excessive computational resources. In this context, a ship HRRP is represented by a complex amplitude vector and a real position vector. The bi-iterative algorithm is designed to alternatively optimize the two vectors. When the latter is held constant, the former is optimized using the sparse recovery through iterative minimization algorithm based on the lognormal ship HRRP model and the CGIG sea clutter model. When the former is held constant, the latter is optimized using the quasi-Newton algorithm. Simulation and measured data are employed to examine the proposed bi-iterative algorithm. The experiments demonstrate that it provides better estimates of ship HRRPs in shorter CPU time compared to the existing algorithms.