Motion Compensation for Airborne SAR via Parametric Sparse Representation

A method of motion status estimation of airborne synthetic aperture radar (SAR) platform in short subapertures via parametric sparse representation is proposed for high-resolution SAR image autofocusing. The SAR echo is formulated as a jointly sparse signal through a parametric dictionary matrix, which converts the problem of SAR motion status estimation into a problem of dynamic representation of jointly sparse signals. A full synthetic aperture is decomposed into several subapertures to estimate the dynamic motion parameters of a platform, and SAR motion compensation is achieved by refining the estimation of the equivalent platform motion parameters, i.e., the azimuth velocity and the radial acceleration of the radar platform, at each subaperture in an iterative fashion. Experimental results based on both simulated and real data demonstrate that: 1) the proposed algorithm outperforms the map-drift algorithm and the phase gradient autofocus algorithm in terms of the imaging quality and 2) compared to the iterative minimum-entropy autofocus, the proposed algorithm produces the comparative imaging quality with less computational complexity in complex motion environment.