Noise-Robust ISAR Imaging for GEO Targets Based on GAPSO-GRFT

Translational motion compensation for geosynchronous orbit (GEO) targets represents a crucial yet challenging task in inverse synthetic aperture radar (ISAR) imaging. The maneuvering tendencies of GEO targets introduce nonlinear distortions to the range profile, posing significant difficulties for precise translation compensation. Additionally, the vast distance between the radar and GEO targets results in an extremely low signal-to-noise ratio (SNR) of the echo signals, thereby undermining the coherence between consecutive pulses. In light of these challenges, this paper introduces a robust method for translational motion compensation tailored to GEO targets. This approach leverages genetic algorithm improved particle swarm optimization (GAPSO) merged with the generalized Radon-Fourier transform (GRFT). Initially, we delve into the intricacies of GEO target behavior to pinpoint an optimal imaging interval. Subsequently, translational motion is modeled using a high-order polynomial and GRFT is utilized to estimate the associated parameters. Furthermore, GAPSO is employed to streamline the parameter search process, thereby reducing computational demands. The proposed method can not only achieve effective motion compensation under ultra-low SNR conditions but also maintain high computational efficiency. Both simulated results and electromagnetic calculation data results affirm the efficiency and robustness of our proposed method.