Abstract
The rapid development of compressive sensing (CS) shows that it is possible to recover a sparse signal from very limited measurements. Synthetic aperture radar (SAR) imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem. For multichannel SAR imaging based on CS, each channel requires sufficient samples for separate imaging and the total number of samples could still be large. We propose an imaging algorithm based on distributed compressive sensing (DCS) that reconstructs scenes jointly under multiple channels. Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene, but also exploits the correlation among channels. It requires significantly fewer samples than multi-channel SAR imaging based on CS. If multiple channels offer different sampling rates, DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.
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