Separation And Imaging Of The Anisotropic Scattering Targets In Wide-angle SAR And Blind Deblurring

As an advanced remote sensing system,Synthetic Aperture Radar(SAR)has been widely used in the past few decades.The idea of wide-angle observation is thus proposed in recent years and employed to formulate a larger synthetic interval.In wide-angle synthetic aperture radar(SAR),the scattering behavior of many illuminated objects might vary with the observation angle,which results in the degradation of the resolution and interpretability of the reconstructed imagery.The aspect dependent scattering characteristic of targets may be submerged by coherent speckles,which is unfavorable for SAR image interpretation and target recognition.In addition,the phase error of SAR system will cause image blur,so we must adopt adaptive phase compensation technology.The self-focusing algorithm can solve the above problems,but the effect is not ideal in the case of low signal-to-noise ratio and two-dimensional defocus.Recently,image blind deblurring technique has been applied to SAR phase error image refocus processing.Therefore,this paper mainly studies the imaging of the Anisotropic Scattering Targets in Wide-Angle SAR and the blind deblurring of SAR image.The main work contents are as follows:Firstly,the wide-angle SAR signal model and anisotropic scattering signal model are introduced in this paper.In order to solve the problems caused by anisotropic scattering,the commonly used matched filtering method,subaperture method and Generalized Likelihood Ratio Test(GLRT)method are introduced.In order to solve the problems existing in the traditional imaging algorithm for wide-angle SAR observation,we introduce a sparse-based source separation and imaging method in this paper.The distinct scattering behaviors of the isotropic and anisotropic scattering targets are employed to formulate a composite projection operator.Then,the sparse constraint is utilized to suppress the cross-projection energy and imaging of the mix-received wide-angle SAR data.Finally,the imagery of the anisotropic scattering targets could be derived with improved focal quality and interpretability.Numerical simulations could verify the validity of the proposed methodology.The resolution and resolution of the reconstructed image have been significantly improved.In order to solve the problem that the self-focusing algorithm is difficult to solve the problem of SAR phase error image blurring in the case of low SNR and 2D defocus,we propose a blind image deblurring using row–Column sparse representations.Firstly,we preprocess the self-focusing and Despeckle noise of the SAR image.Then we model the outer product of kernel and image coefficients in certain transformation domains as a rank-one matrix,and recover it by solving a rank minimization problem.Our central contribution then includes solving two new optimization problems involving RCS to automatically determine blur kernel and image support sequentially.The kernel and image can then be recovered through a singular value decomposition.Finally,since the SAR image is complex data,we deblur its real part and imaginary part respectively.We combined the results to get the final clear SAR image.Numerical simulations could verify the validity of the proposed methodology.