ISAR Imaging Method Based On Sparse Aperture

Inverse synthetic aperture radar(ISAR)can achieve long-range,all-time,all-weather imaging of spatial,aerial,marine and other targets,as well as reflect the spatial scattering coefficient distribution,size and motion characteristics of the target,which is an important means of acquiring target information and widely used in military and civil fields.With the increasingly complex electromagnetic environment and the diversity of radar functions and tasks,ISAR imaging may face problems such as missing echo aperture and discontinuity,and imaging performance degradation under low SNR conditions.At the same time,the existing ISAR signal system also has defects,such as the coupling effect of distance and velocity in LFM signal,and the long observation time required for stepping frequency signal.Aiming at the above problems,this paper studies the ISAR imaging method based on sparse aperture,including the establishment of ISAR sparse aperture echo model,the improvement of weighted compressed sensing imaging under low SNR,the estimation of adaptive echo pulse number of sparse aperture imaging,and the two-dimensional imaging method based on V frequency modulation(VFM).The first chapter illustrates the background and significance of this paper,and presents the development process of ISAR imaging and the research status of ISAR imaging technology based on compressed sensing;emphatically introduces two typical ISAR imaging systems.Besides,it summarizes the application and development of compressed sensing technology in ISAR imaging.This chapter analyzes and summarizes the problems that need further study in ISAR imaging;introduces the main content of this paper;and briefly introduces the work and arrangement of this paper.The second chapter firstly deduces the ISAR imaging process from the turntable imaging model,constructs the sparse aperture echo model,reconstructs the two-dimensional image,and expounds the basic principle and process of compressed sensing high-resolution ISAR imaging.Secondly,a weighted compressed sensing ISAR imaging method is improved for low SNR.In order to overcome the problem that the weighted coefficient greatly affects the noise constraint and image stability,a new weighted coefficient updating rule and reconstruction imaging process are proposed.According to the characteristics of the target,the optimal weighting coefficient was obtained through the adaptive iteration of the signal support set,and the final ISAR imaging result was obtained through the weighted compressed sensing reconstruction.The reconstruction performance and anti-noise performance were significantly improved under the condition of low SNR.In chapter three,an adaptive ISAR imaging pulse estimation method is proposed for the optimization of radar resources and the application of sparse aperture imaging.An adaptive iterative optimal threshold segmentation method is proposed to separate the signal from the background element.Finally,the minimum ISAR imaging sparse aperture is estimated to ensure the imaging quality,which can effectively improve the utilization efficiency of radar resources.In chapter 4,the ISAR imaging process of V frequency modulation(VFM)signal is studied,and a two dimensional coupled compressed sensing imaging method is proposed.Firstly,the characteristics and ambiguity functions of VFM signal are analyzed.Aiming at the frequency shift problem,two pulse compression methods of two-channel matched filtering and dechirping are proposed.The simulation results show that the former is more adaptive to noise,while the latter can effectively reduce the sampling rate.Secondly,according to the fact that the target occupies only a few pixels in the imaging plane,the two-dimensional sparse echo model of ISAR target of VFM signal is established.The 2D-SL0 algorithm can effectively reduce the required amount of data and computational complexity.The fifth chapter summarizes the whole work and looks forward to the next research work.