Range DBF SAR Imaging Based on Compressed Sensing

The rapid development of compressed sensing (CS) shows that compressible signals can be captured and reconstructed at a rate significantly below the Nyquist sampling rate. Synthetic Aperture Radar (SAR) imaging based on range digital beam-forming (DBF) technique can realize wide swath target scene by requiring each channel sufficient samples, while bring about the total volume of samples to be very dense and consequently leading to extremely high data rate on satellite down link system. This article introduces a novel imaging algorithm for range DBF SAR system based on CS which can infer an accurate recovery of the target scene through an optimization process. Futhermore, the new algorithm can eliminate the disadvantage of magnitude fluctuation in conventional filtering imaging. This paper provides the principles and theories of compressed sensing, analyzes the sparsity of the signals in the model of digital beam-forming, and finally puts forward a sparse imaging scheme based on compressed sensing. Simulation result testifies the validity and favorable performance of the new imaging scheme, which shows that the new technique can reduce the system data amount significantly, make the data acquisition more efficient and advance the imaging quality.