Parametric Translational Compensation for ISAR Imaging Based on Cascaded Subaperture Integration With Application to Asteroid Imaging

Abstract

Translational compensation is a crucial step in inverse synthetic aperture radar (ISAR) imaging. However, nonparametric compensation methods may collapse under the condition of a low signal-to-noise ratio (SNR), and high-order parametric compensation methods usually have a large computational load. In this article, two cascaded integration methods are proposed to solve the problems above based on the generalized Radon-Fourier transform (GRFT). The first method models the translational motion as polynomial and utilizes the proposed subaperture GRFT (SAGRFT), which is a fast implementation of the GRFT, to estimate the translational parameters. The SAGRFT divides the full aperture into several subapertures and realizes coherent integration by implementing moving target detection (MTD) within subapertures and the GRFT among subapertures. In addition, the distribution property of the blind speed side lobes (BSSLs) generated by the SAGRFT is analyzed. On this basis, the second method based on metaheuristic algorithms is proposed to further accelerate the parameter estimation process and solve the BSSLs problem. The proposed methods can not only play a role in stealth target imaging but also be utilized in near-Earth asteroid (NEA) imaging in radar astronomy. Finally, the numerical simulations of asteroid imaging and the experimental results of plane imaging are demonstrated to verify the performance advantages of the proposed methods.