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

The authors address the autofocusing problem in synthetic aperture radar im agery 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 em ploying fractional lower-order statistics (FLOS) of the phase-history data. The introduced FLOS-based methods perform a nonlinear transformation of th e radar measurements, can mitigate the effects of impulsive additive clutte r, and include the conventional algorithms as special cases. The benefits o f the proposed approach are quantified by means of simulations, and the new FLOS-based methods are compared to current state-of-the-art processing wit h real synthetic aperture radar imagery data.