Research On Airborne SAR Super-resolution Imaging Processing Based On Sparse Representation

The system platform of airborne synthetic aperture radar(SAR)has the characteristics of high flexibility,two-dimensional high-resolution mapping performance and all-weather work.It has been widely used in military and civil fields.However,due to the limitation of many factors,such as hardware cost,natural environment and so on,the reconstructed image quality of SAR system may not reach the expected goal.Using image post-processing to improve the resolution has become one of the hotspots in the field of remote sensing imaging in recent years.For the airborne circular SAR system,this paper studies the super-resolution imaging technology of sub aperture and full aperture data respectively to achieve the sidelobe suppression of the reconstructed point target response function and the improvement of image resolution.Firstly,the sparse image processing based on the nonlinear frequency modulation signal is studied.The side lobe suppression of the reconstruction response function is realized by using the nonlinear frequency modulation characteristics and sparse constraints,so as to avoid the masking of the strong scattering point target to the adjacent weak target.Then,the polar format algorithm(PFA)in bunching mode is studied.A two-dimensional sparse constraint reconstruction based on iterative threshold algorithm(ITA)is proposed to suppress the sidelobe of point objective response function.Finally,SAR image super-resolution processing based on dictionary sparse representation is studied.The image feature is extracted by one step and two step feature methods,and the joint training dictionary is generated by the joint training of high-resolution image and low-resolution image,which is solved by the local sparse constraint method.The simulation results provide a reliable verification for the research in this paper.