Research On High-resolution Radar Imaging Method Based On Compressed Sensing

Synthetic aperture radar(SAR)and inverse synthetic aperture radar(ISAR)imaging technique has the characteristics of long range detection,all-time and all-weather work,which effectively enhance the ability of information sensing and data acquisition of the modern radar.Therefore,SAR/ISAR imaging technique is widely used in many military and civil fields.As SAR and ISAR improve the imaging resolution,restricted by the Nyquist sampling theorem,they face the problems of high sampling rate and large amount of data.The problems become more prominent when it comes to multidimensional observations,such as multiple antennas,multiple polarizations and multiple views.For modern multi-function and networked radar,the time and resources used for wideband imaging of a single target are limited,resulting in the data discontinuity and missing,thereby influencing the imaging resolution.The newly proposed theory of compressed sensing(CS)provides a new way to solve the above problems in traditional radar imaging,which has shown tremendous potential in reducing the sampling rate of the radar data,making up for the data missing and improving the quality of image reconstruction,etc.Based on the CS theory and its application in radar imaging,this dissertation studies high-resolution radar imaging methods based on CS to solve the problems of the CS applications in ISAR imaging of complex targets,multichannel SAR and wide angle SAR imaging.Main contributions and innovative achievements of this dissertation are as follows:1.As traditional range-instantaneous Doppler(RID)imaging method is of low resolution and has high requirements for the integrity of the data,a range-instantaneous Doppler(RID)imaging method of maneuvering targets based on CS is proposed.Exploiting the spatial sparsity of the target scene,the target echo is sparsely represented in the time-frequency domain;the instaneous dictionary is constructed to obtain the range-instaneous Doppler images of different moments;in order to enhance the energy of the real target and suppress the noise,the strategy of weighted iteration is introduced.Both simulation and experimental results show that this method has higher resolution and more robust imaging performance in the case of low signal-to-noise ratio and the data missing.Furthermore,this method is extended to interferometric ISAR three-dimensional(3D)imaging of maneuvering targets.The interferometric channels are jointly sparsely represented to enhance the estimation performance of the height information of scatterers.2.Due to the grid error and inaccurate estimation of the spinning angular velocity,the imaging performance is affected considerably.In order to solve this problem,a robust narrowband radar imaging method based on CS is proposed firstly,which can effectively alleviate the influence of the grid error on imaging performance,by optimizing the imaging model and improving the reconstruction algorithm.Secondly,a joint method with CS imaging and the optimal estimation of spinning angular velocity is proposed,which can obtain the best estimation of the spinning angular velocity and the optimal reconstruction of target,by iterative research of the spinning angular velocity based on the image entropy of the imaging result.In order to solve the problem of high computational complexity of conventional wideband radar 3D imaging methods of spinning targets,a new 3D imaging method of spinning targets is proposed.This method combines narrowband radar 2D imaging and interferometric processing to obtain 3D images of the target,which effectively reduce the complexity of the imaging method.3.Traditional multichannel SAR imaging uses independent processing of each channel,which ignores the correlation between channels and cannot guarantee the consistency of the positions and the number of scatterers in multichannel images.To solve this problem,a multichannel SAR high-resolution imaging method based on joint sparse reconstruction is proposed.This method uses the prior information of the same sparsity support over multichannel images,constructs the joint constraint function integrating multichannel information to realize the consistency of the positions and the number of scatterers in different channels,and uses the improved reconstruction algorithm to achieve the joint reconstruction of multichannel SAR images.The experimental results show that this method can obtain high-resolution SAR images with limited measurements and the positions and the number of scatterers are aligned in different channels,which improves the ability of information acquisition of the target.4.Traditional subaperture composite imaging method of wide-angle SAR is easily affected by the registration errors and the anisotropic scattering behavior of the target,resulting in some desultory points existed in the composite image.To solve this problem,a joint sparse reconstruction method of subaperture images for joint target space reconstruction and azimuth dependent scattering feature extraction is put forward.This method exploits the correlation between different subaperture images,defines the mixed norm with comprehensive information of full aperture,and constructs the joint sparse reconstruction model of all subaperture images constricted by the mixed norm,which can realize the precise match and suppress desultory points in the composite image.Considering the continuity of the target scattering characteristics between the adjacent subapertures,a smoothness constraint for subaperture images along the azimuth dimension is used to make the extracted azimuth dependent scattering characteristics more accurate.