Research On Imaging Algorithm Of Airborne Passive Synthetic Aperture Radar Based On Sparse Reconstruction

With the development of radar technology,passive radar has been applied to recognizing ground targets and monitoring nautical targets.Target identification is inaccurate in practice because of existing terrain and obstacles.Receivers of the Airborne Passive Synthetic Aperture Radar(SAR)system are on the airborne platform to receive the external radiation source signal to achieve the target imaging.It can cover a larger surveillance area and easily make receivers close to the targets without being detected by mobility of the aircraft platform.Due to its characters,the airborne passive SAR system is widely used in military applications.In this thesis,airborne passive wide-angle SAR sparse imaging reconstruction and multistatic airborne passive extended target sparse imaging reconstruction are mainly researched in the imaging of airborne passive SAR system.The principal research contents of this thesis are as follows:(1)Firstly,the working principle of airborne passive SAR system is introduced.Then,the China Mobile Multimedia Broadcasting signal model is established.Finally,the Compressed Sensing theory for sparse representation of signals is introduced.The greedy algorithm is used to solve the local optimal solution to obtain the relative optimal solution,and then the sparse imaging is realized.(2)Aiming at the problem of the aspect angle dependence of target scattering characteristic for airborne passive wide-angle synthetic aperture radar(SAR),the thesis proposes a imaging method based the simultaneous orthogonal matching pursuit(SOMP).Firstly,the algorithm divides the wide aperture into several sub-apertures.Then the thesis introduces a forward signal model of airborne passive wide-angle SAR and then formulates the imaging problem as an joint sparsity optimization problem of multiple measurement vectors.Next the scattering characteristics of the target are described by a joint sparse model based on multiple measurement vectors.Finally,distributed greedy sparse reconstruction method based SOMP can achieve the accurate and robust imaging reconstruction of the observed ground scene.The simulation results have shown that the distributed greedy sparse reconstruction method based on SOMP has low imaging reconstruction errors and has profound practical meaning.(3)Aiming at the sparseness of the detection scene,a multistatic airborne passive scalability target sparse imaging reconstruction method is proposed.The method utilizes the clustering characteristics of the target so that the radar image corresponding to each transmitting station has a block sparse feature.At the same time,because of different azimuth angles,and the corresponding non-zero reflection coefficients of each transmitting station has the same position but different values.The radar images corresponding to all transmitting stations have the characteristics of joint sparsity,and Two Level Block-sparsity Matching Pursuit(TLBMP)imaging algorithm based on probabilistic graph model is proposed.The simulation results have shown that the TLBMP algorithm has smaller reconstruction error,reduces the complexity of the algorithm and speeds up the image reconstruction.