Sparsity-based Autofocus for Under-sampled Synthetic Aperture Radar

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Motivated by the field of compressed sensing and sparse recovery, nonlinear algorithms have been proposed for the reconstruction of synthetic-aperture-radar images when the phase history is undersampled. These algorithms assume exact knowledge of the system acquisition model. In this paper we investigate the effects of acquisition-model phase errors when the phase history is undersampled. We show that the standard methods of autofocus, which are used as a postprocessing step on the reconstructed image, are typically not suitable. Instead of applying autofocus in postprocessing, we propose an algorithm that corrects phase errors during the image reconstruction. The performance of the algorithm is investigated quantitatively and qualitatively through numerical simulations on two practical scenarios where the phase histories contain phase errors and are undersampled.
Original languageEnglish
Pages (from-to)972 - 986
Number of pages15
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number2
Publication statusPublished - 30 Apr 2014

Keywords / Materials (for Non-textual outputs)

  • Synthetic Aperture Radar, Autofocus, Compressed Sensing, Sparse Recovery, Blind Calibration, Block Relaxation Methods, Phase Retrieval


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