GPU-Accelerated SAR Image Formation in the Presence of Very Large Motion Error

Synthetic Aperture Radar (SAR) systems sense electromagnetic backscatter from scenes generated from a sequence of excitation pulses of RF radiation emitted from the radar antenna varying spatial positions. Focusing the radar returns into coherent images requires highly accurate knowledge of the antenna position for the duration of the pulses. In this article a massively parallel approach is propose to solve the NP-hard problem of focusing radar data collected in the presence of large motion errors. Little research has been dedicated to the development of focusing algorithms capable of image formation when motion error magnitudes exceed the nominal wavelength of the radar excitation signal. This problem has been shown to be non-deterministic polynomial-time hard (NP-hard) to solve and computational challenges are exacerbated by the high computational cost of associated with the SAR focusing algorithms needed to conduct the search. The proposed approach seeks to address these challenges by restricting trajectories to smooth (low-order) curve trajectories and applying an optimized massively parallel GPU implementation of the SAR focusing algorithm to search over candidate trajectories for the trajectory yielding a focused SAR image.