A ISAR Imaging Method for Space Targets Based on Fast Estimation of Joint Motion Parameters

Inverse Synthetic Aperture Radar (ISAR) is an important tool for imaging and monitoring space targets. The large rotation angle of space targets can exacerbate the phenomenon of Migration Through Resolution Cells (MTRC), seriously affecting the ISAR imaging performance. For the fast estimation and compensation of echo phase errors caused by the motion of space targets, this paper proposes an ISAR space-target imaging method based on the rapid estimation of joint motion parameters. This method combines the advantages of the high efficiency of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm and the high compensation accuracy of the Polar Format Algorithm (PFA) algorithm. The proposed method formulates an image entropy minimization model considering the joint estimation of the translation and rotation parameters of the target. To reduce the possibility of optimization falling into local optima, the proposed method solves sub-steps, which comprise rough and fine estimations of the target motion parameters, using the BFGS optimization algorithm. The proposed method rapidly estimates target rotation parameters and performs quick MTRC compensation under large rotation angles. The simulation of point targets and imaging results of actual civil aircraft data show that compared with the Particle Swarm Optimization-Polar Format Algorithm (PSO-PFA) algorithm, the proposed method estimates motion parameters with a higher accuracy under low signal-to-noise ratio conditions. Further, the computational efficiency is improved by more than five times, which is significantly advantageous.