Synthetic Aperture Radar Imaging And Phase Error Correction Based On Compressed Sensing

Traditional imaging radar system usually transmit wide-band signals to improve itsimaging resolution, but wide-band signals face a challenge of high sampling rate. In resentyears, the newly-emerging theory named compressed sensing indicated that, a sparsesignal can be exactly recover from the measurements sampled lower than Nyquistsampling rate. Compressed sensing may effectively reduce the data rate of imaging radarsystem and release the data transition burden. Therefore, this paper researches on the radarimaging algorithm based on compressed sensing theory.Firstly, a new range imaging algorithm based on compressed sensing is proposed. Inthe study of the method, firstly discretize the scene area be observed, and then analyze therelationship between the echoes and the discrete scene to establish a spare representationmodel of the echoes which similar with the mathematical model in the compressed sensingtheory. So the high resolution range image can be obtained form a few uncorrelatedmeasurements by an appropriate reconstruction algorithm. The effectiveness of theproposed algorithm is tested through simulation data experiment.Secondly, in the study of the Synthetic Aperture Radar imaging algorithm based oncompressed sensing theory, a sparse representation model of the echo signals can beestablished through the vectorization of discrete scene and the echo data, and take thespatial locations of the scene into account. The two dimensional image reconstructed formthe model based on compressed sensing possesses high resolution than traditional RangeDoppler imaging algorithm. The effectiveness of the proposed algorithm is demonstratedthrough simulation data experiment.Finally, this paper consider space-invariant and space-variant phase error that exist inecho data, therefore, the corresponding phase error correction algorithms are proposed.The algorithm is an iterative algorithm. Every iteration involves three step, namely fieldestimation、phase error estimation and model matrix update. The defocus in the imagingcaused by phase error can be gradually eliminated by the iterative method. And finally, the algorithms produce high resolution focused Synthetic Aperture Radar images. Theeffectiveness of the proposed algorithm is demonstrated through simulation dataexperiment.