Space Target Full Polarization Sparse Imaging Technology Based On Multi-objective Evolutionary Algorithm

As an important means of acquiring space target information,imaging radar can acquire high-resolution images of targets at all times and all days.Sparse imaging of targets has the advantages of good imaging performance and lower requirements for data acquisition,so it has become a hot spot and frontier in radar imaging research.Aiming at the problems of regularization parameter selection and difficulty in using prior information in classic sparse imaging,this paper proposes a multi-target sparse recovery imaging framework.Based on this framework,a fully polarized sparse random step frequency ISAR imaging method is studied.The main research contents and results are as follows:The first chapter is the introduction of this paper,and gives the research background and significance of the thesis.The research status of radar imaging,polarization radar and ISAR sparse imaging method are introduced respectively.Finally,the main work and organizational structure of this paper are summarized.The second chapter introduces the principle of ISAR sparse imaging,and summarizes the main sparse imaging algorithms,and proposes the performance evaluation method of the algorithm.The basic principles of ISAR imaging are explained firstly.Then an ISAR sparse recovery imaging model was established.After that,a variety of classic sparse recovery algorithms are reviewed,and the corresponding algorithm flows are given.Finally,the performance evaluation index and index calculation method of the algorithm are proposed,and the imaging performance of various algorithms is compared via dark room measurement data,which provides a favorable basis for the algorithm selection of sparse imaging.The third chapter studies the synthesis method ofThe fourth chapter is the joint autofocus imaging and azimuth calibration of fully polarized ISAR.Based on the high-quality synthesis results of the full-polarization high-resolution one-dimensional range image in Chapter 3,the research on sparse recovery and azimuth calibration of fully polarized ISAR images is carried out.Firstly,the one-dimensional distance image model is azimuthally expanded.By proposing the translation model and the rotation model of the target,the motion components of the original signal model are combined corresponding to the phase terms,and the signals of the various bits are stacked to form a full polarization.Azimuth signal parameterization joint sparse representation model.The multi-objective optimization model of ISAR combined with autofocus imaging and azimuth calibration is constructed by using the idea of distance image synthesis.Under the framework of particle swarm optimization algorithm,the three targets are optimized by alternating iteration to obtain the global optimal solution.The experimental results show that the joint azimuth motion parameter estimation and autofocus imaging method not only have higher accuracy for estimating the motion parameters,but also better reconstructed two-dimensional ISAR image quality.The fifth chapter is the concluding remarks,summarizes the research content of the full text,and looks forward to the future research direction.