  A common problem that arises in radar imaging systems, especially those
mounted on mobile platforms, is antenna position ambiguity. Approaches to
resolve this ambiguity and correct position errors are generally known as radar
autofocus. Common techniques that attempt to resolve the antenna ambiguity
generally assume an unknown gain and phase error afflicting the radar
measurements. However, ensuring identifiability and tractability of the unknown
error imposes strict restrictions on the allowable antenna perturbations.
Furthermore, these techniques are often not applicable in near-field imaging,
where mapping the position ambiguity to phase errors breaks down.
  In this paper, we propose an alternate formulation where the position error
of each antenna is mapped to a spatial shift operator in the image-domain.
Thus, the radar autofocus problem becomes a multichannel blind deconvolution
problem, in which the radar measurements correspond to observations of a static
radar image that is convolved with the spatial shift kernel associated with
each antenna. To solve the reformulated problem, we also develop a block
coordinate descent framework that leverages the sparsity and piece-wise
smoothness of the radar scene, as well as the one-sparse property of the two
dimensional shift kernels. We evaluate the performance of our approach using
both simulated and experimental radar measurements, and demonstrate its
superior performance compared to state-of-the-art methods.
