A Motion State Judgment and Radar Imaging Algorithm Selection Method for Ship

Abstract

Radar imaging for ships is hard because of the unpredictable motion states of ships. Existing ship radar imaging methods usually do not take the effects of different motion states into account, which leads to a degraded imaging result when the utilized imaging algorithm cannot match the target motion state. To solve this problem, a motion state judgment and radar imaging algorithm selection method is proposed, whose keys are to estimate the motion parameters of scatter points on the ship, judge the target motion state based on the space-variant features of the estimated motion parameters, and further choose a proper imaging algorithm to achieve the radar imaging result with higher quality. In this article, the radar imaging model of ship is first constructed, and the spatial variance features of motion parameters are quantitatively analyzed. Next, an improved motion parameter estimation method utilizing generalized Radon–Fourier transform (GRFT) modified by sidelobe-learning particle swarm optimization (SSLPSO) and relaxation (RELAX) technique is proposed, which can solve the performance reduction caused by the unnecessary values introduced by the method based on traditional GRFT and realize accurate motion parameter estimation. Then, based on the estimated motion parameters, a motion state judgment and radar imaging algorithm selection method, which takes the targets’ motion state, theoretical resolutions, as well as the effect of high-order phase error into consideration, is proposed to obtain a high-quality and high-resolution radar image. Finally, computer simulation and experimental results of GaoFen-3 (GF-3) satellite single channel data validate the proposed method.