SAR images reconstruction based on Compressive Sensing

Chirp signals are transmitted by Synthetic Aperture Radar (SAR) and the received signals are sampled into Inphase and Quadrature components which are so-called raw SAR data. The data is so tremendous that it brings extraordinarily high burden to the on-board storage and downlink bandwidth. This paper addresses a new process of the raw SAR data by sampling the data below Nyquist rate in terms of Compressive Sensing, which shows that super-resolved data can be reconstructed from an extremely small set of measurements than what is generally considered necessary. A wavelet-based contourlet transform, a multi-scale random Gaussian sampling, and a stage-wise directional pursuit are cooperating in this new process framework to realize our purpose, and it turns out that with only above 20% of the original transmission data should we reconstruct SAR images promisingly. Two major improvements of this radar transmission system are achieved: (a) potentially low ¿information rate¿ is preferred rather than high Nyquist rate while the transmission end emit the raw data, and (b) the hardware is significantly alleviated that lots of resources and energy can be saved in the manufacture process. This idea could enable the alleviation of transmission burden, reducing the sampling rates, the transmission time, the measurement time dramatically, shifting the emphasis from expensive transmission hardware to three smart gradients of CS framework.