Multichannel SAR Autofocus using multiple low-return constraints

MultiChannel Autofocus (MCA) and Reversed-step MCA (RMCA) for Synthetic Aperture Radar (SAR) assume a region of low return in the focused data, and work best when the region is known accurately. In practice, the returns from within the side-lobes of the antenna footprint are highly attenuated; thus a region of highly-probable low returns is provided. However, scene-dependent features like strong reflectors in the presumed low-return region may violate the assumptions underlying MCA and RMCA, with resulting performance degradation. We consider such a scenario and propose scene-independent, random selection methods of the low-return constraints. Our simulation results show that a well-focused image can be obtained within a few trials, even when the number of anomalies is moderately large.