Raw SAR data compression by structurally random matrix based compressive sampling

Synthetic aperture radar (SAR) is an imaging system which can provide high resolution images of earth surface. It transmits chirp signals and the received echoes are sampled into I and Q components, thus producing a huge amount of raw SAR data which may exceed the on-board storage and downlink bandwidth. In this paper, we compress the raw SAR data by sampling the signal below the well-known Nyquist rate using a novel framework of compressive sampling (CS), i.e, a fast and efficient sampling with structurally random matrices(SRM) which is developed on the provable mathematical model. In this framework, a 2DFFT and a structurally random matrix whose columns are permuted randomly are employed in the encoder. At the decoder the basis pursuit reconstruction then proceeds to find the sparsest signal. Simulation results are also presented to prove the feasibility of our proposed scheme.