High-resolution autofocus techniques for SAR imaging based on fractional lower-order statistics

The authors address the autofocusing problem in synthetic aperture radar imagery by introducing techniques that achieve robust image formation in the presence of severe heavy-tailed clutter and noise. Current state-of-the-art methods are extended, which are based on second-order moment theory, by employing fractional lower-order statistics (FLOS) of the phase-history data. The introduced FLOS-based methods perform a nonlinear transformation of the radar measurements, can mitigate the effects of impulsive additive clutter, and include the conventional algorithms as special cases. The benefits of the proposed approach are quantified by means of simulations, and the new FLOS-based methods are compared to current state-of-the-art processing with real synthetic aperture radar imagery data.