RANDOM STEP FREQUENCY CSAR IMAGING BASED ON COMPRESSIVE SENSING

Abstract

Circular synthetic aperture radar (CSAR) imaging based on compressive sensing with random step frequency (RSF) as transmitted signal is introduced. CSAR is capable of obtaining both two-dimensional high resolution image and three-dimensional image due to a circular collection trajectory. RSF signal shares good characteristics of noise signals including thumbtack-shape ambiguity function, low probability of interception, and strong anti-jamming capability. As a result, CSAR adopting RSF signal can make use of advantages of both CSAR and RSF signal. Compressive sensing is a new data acquisition and reconstruction theorem for sparse or compressible signals, which needs fewer samples to reconstruct signals than traditional Nyquist theorem. Simulation results show that both two-dimensional and three-dimensional targets can be well reconstructed from few samples by applying compressive sensing to RSF CSAR imaging.