Research On Radar High-resolution Imaging Technology Based On Compressed Sensing Of Alternating Direction Multiplier Metho

Radar images are now extensively used in many scientific fields such as ground target reconnaissance,earth environment surveying and mapping,oceanography and glacier research.Because the radar high-resolution images of ground and ocean targets contain key information such as the shape and size of the target,the quality of radar imaging has a great influence on the feature extraction and recognition of the target.In actual situations,radar echoes often have problems such as low signal-to-noise ratio and incomplete data,which affect the imaging quality.Signal sparse representation is the most popular research direction of signal analysis.The use of observation data for sparse representation of signals and in-depth exploration of the sparse nature of signals provides a new solution to the problem of radar high-resolution imaging.The main research content of this thesis is systematically introduced from the following three parts:The initial part introduces the theory of signal sparse representation and the basic principles of compressed sensing,presents orthogonal matching tracking,sparse Bayes and other fusion algorithms based on signal sparse representation theory,and finally introduces GTD scattering center modeling theory and linear array three-dimensional(3D)SAR imaging theory.In the second part,a sparse reconstruction algorithm based on the alternate direction multiplier method based on compressed sensing is proposed.According to the two-dimensional radar echo sparse representation model,for the known radar echo data,Bayesian compressed sensing(BCS)and Alternating Direction Method of Multipliers(ADMM)are used to solve the sparse signal and realize multi-radar echo data fusion Obtain full echo data,and complete high-resolution imaging.It is analyzed that in different scenes and noise environments,different fusion methods are used to acquire high-resolution radar images of multiple radar echo data fusion,and it is verified that ADMM has strong robustness in two-dimensional data fusion imaging.In the third part,a multi-dimensional ADMM sparse reconstruction algorithm is proposed to achieve high-resolution 3D radar imaging.First,an airborne linear array 3D synthetic aperture radar imaging model is established,and the 3D imaging method of radar based on compressed sensing is studied,which improves the image quality of radar 3D imaging.Secondly,for the stationary and moving target imaging scenes of the airborne linear array 3D synthetic aperture radar,under the conditions of uniform sampling and non-uniform sampling,using the internal connection of the radar 3D echo data,the multi-dimensional compressed sensing algorithm ADMM is used to carry out the 3D data.Processing solves the problem of poor target imaging quality under sparse sampling.