Research On Sparse Aperture ISAR Imaging Technology Based On Meshless Compressed Sensing

As a long-range,all-day and all-weather microwave imaging technology,inverse synthetic aperture radar imaging has been the key development object in various countries and has been widely used in military and civil fields such as aviation,air defense,space surveillance,and missile defense.During radar observation,there are many reasons result in the loss of the pulse echo,which is called sparse aperture.Under sparse aperture conditions,Range-Doppler imaging shows main lobe broadening and is affected by clutter.Compressed sensing technology is a basic method to deal with the problem of sparse aperture,but the traditional CS algorithm assumes that the strong scattering point of the target is located on the grid.Yet in practice,the distribution of the scattering point is continuous.If it deviates from the grid,the performance of CS will deteriorate.Therefore,a sparse aperture ISAR imaging method based on gridless CS is proposed in this paper.The method does not need to discretize the scene and use atomic norm minimization as sparse constraint.By solving semidefinite program,the position of scattering centers can be estimated directly in continuous space,which can solve the problem of basis mismatch effectively.At the same time,in view of the problem that the traditional self-focusing algorithm cannot effectively correct primary phase error under the condition of sparse aperture,combined with the gridless compressive sensing sparse reconstruction technology,the sparse aperture ISAR self-focusing algorithm with joint constraints of entropy and sparseness is introduced.It can still get good self-focusing result at low aperture sparsity condition.This paper takes ISAR imaging technology under sparse aperture as the research target,and explores the sparse signal reconstruction problem based on gridless compressed sensing theory and the sparse aperture ISAR autofocus problem based on the joint constraint of entropy and sparse.In view of the above,the main work of this article can be divided into the following parts:The first part first introduces the research background and significance of sparse aperture ISAR imaging technology,expounds the development of sparse aperture ISAR imaging technology,and gives the basic framework of this paper.Then,ISAR's translational compensation and distance Doppler imaging methods are briefly introduced,as well as the effects of sparse apertures,laying the foundation for subsequent chapters.The second part mainly studies the sparse signal reconstruction theory based on gridless compressed sensing.The sparse aperture ISAR echo model is introduced first,and then the basic theory of compressive sensing is briefly introduced.The problem of basis mismatch in traditional compressive sensing algorithms that require grid discreteness is analyzed.Based on this,a gridless compressive sensing algorithm based on atomic norm minimization is introduced.The concept of atomic norm,the principles and implementation methods of one-dimensional and two-dimensional gridless compressed sensing algorithms are introduced.Finally,through simulation experiments,the applicability of the algorithm in solving sparse aperture ISAR imaging problems is verified.The third part studies the sparse aperture ISAR self-focusing technology.Firstly,the sparse aperture ISAR self-focusing model,and the principles of phase gradient autofocus and image entropy minimization are introduced.Then combining the gridless compressed sensing technology and self-focusing technology,an image entropy minimization algorithm based on the joint constraint of entropy and sparseness is introduced.Finally,simulation experiments verify the effectiveness of the algorithm under sparse aperture conditions.