Synthetic Aperture Radar raw data encoding using Compressed Sensing

Abstract

Synthetic aperture radar (SAR) is active and coherent microwave high resolution imaging system, which has the capability to image in all weather and day-night conditions. SAR transmits chirp signals and the received echoes are sampled into In-phase (I) and Quadrature (Q) components, generally referred to as raw SAR data. Raw data compression is an essential future requirement for high resolution space borne SAR sensor in order to reduce the volume of data that is stored onboard and later transmitted to ground station. Due to the low computational resources available onboard satellite a simple encoding algorithm based on compressed sensing framework to compress SAR raw data with real wavelets is proposed in this paper. The decoding of the data on ground is then based on convex optimization through projections on convex sets (POCS) or uses greedy algorithms such as orthogonal matching pursuit (OMP). The option of converting the complex SAR signal to real data by shifting the frequency spectrum by half bandwidth and then using real wavelets as a sparsifying transform to compress the SAR signal is studied and compared with using the wavelets with the complex signal in the CS framework.