Data Acquisition and Processing of Parallel Frequency SAR Based on Compressive Sensing

Traditional Synthetic Aperture Radar (SAR) utilizes Shannon-Nyquist theorem for high bandwidth signal sampling, which induces a complicated SAR system, and it is difficult to transmit and process a huge amount of data caused by high A/D rate. Compressive Sensing (CS) indicates that the compressible signal using a few measurements can be reconstructed by solving a convex optimization problem. A novel SAR based on CS theory, named as Parallel Frequencies SAR (PFSAR), is proposed in this paper.

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