Research On High Resolution Imaging Algorithm Of Line Array Three Dimensional SAR With Compressed Sensing

As a Synthetic Aperture Radar(SAR) imaging system with 3D spatial imaging capability,line array 3D SAR can overcome the problems such as shadow shielding and spatial ambiguity in traditional 2D SAR imaging.So it has a huge application prospect in military and civil fields.However,the conventional imaging methods of line array 3D SAR are limited by Rayleigh criterion,which lead to the problems such as high sampling rate and low imaging resolution.According to the compressive sensing theory,if the signal is sparse or sparse in a transform domain,the signal can be reconstructed without distortion by using a sampling rate far lower than that required by Nyquist's sampling.Generally,in 3D SAR imaging scene,the target has a strong spatial sparsity,so the compressive sensing theory can be applied in 3D SAR imaging to reduce sampling array and the difficulty of hardware implementation.More importantly,it can break the resolution limit of traditional imaging algorithms to the quality of SAR imaging.The 3D SAR imaging algorithm based on the compressive sensing theory is studied,and further research is carried out to reduce the computation and improve the imaging quality of the algorithm.The main research and innovations contents are as follows:1.The theory of line array 3D SAR imaging and compression sensing are described.Firstly,the geometric model of array 3D SAR and the echo signal model is given,Secondly,the radar fuzzy function is used to analyze the low resolution of the traditional imaging algorithm based on matched filtering.Finally,the basic concept of compressed sensing is described,the linear observation model of array three-dimensional SAR is presented,and the theoretical basis of compressed sensing theory in array threedimensional SAR imaging is provided.2.SAR imaging algorithm based on compressed sensing theory is studied.Firstly,this thesis introduces the flow of OMP algorithm and analyzes the advantages of OMP algorithm,such as high operational efficiency and simple flow,but it need to preset sparsity and is sensitive to noise.However,the(Sparsity Bayesian Recovery via Iterative Minimum) SBRIM algorithm based on Bayesian theory can according to the measuring signal of the prior probability,reasonable modeling,preset sparsity is not need and achieve high resolution imaging in the case of low SNR,but in SBRIM algorithm for SAR 3D scene imaging,because of the need to use all of the range unit to construction of measurement matrix,there is the problem of low efficiency of computation.3.A compressive sensing imaging algorithm based on adaptive threshold is proposed.Aiming at the low efficiency of SBRIM algorithm in SAR 3D scene imaging,a compressive sensing imaging algorithm based on adaptive threshold is proposed.The idea of fuzzy clustering is used to cluster the SAR echo signal,according to the amplitude information of SAR echo signal,a threshold value is generated by using the minimum criterion of non-similarity cost function,the signal above the threshold is constructed into the measurement matrix,so the computation of the algorithm is reduced.Compared with SBRIM,the results of simulation results and the experiment data show that the proposed method improves the image quality and the efficiency of the algorithm.4.A sparse imaging algorithm for 3D SAR based on subarray decomposition and fusion of coprime array is proposed.Compared with the random sampling array commonly used in compressive sensing imaging,the coprime array is easy in hardware implementation,and can suppress the imaging lobe of uniform sparse sampling.However,false targets appear in the imaging when using the coprime array directly combined with the compressed sensing algorithm.Aiming at this problem,this thesis proposes a SAR imaging method based on coprime array,which uses compressive sensing imaging algorithm based on coprime array and subarray.Then the imaging results were fused to suppress the grating-lobes and false targets.Based on uniform array and coprime array,SBRIM algorithm and the algorithm of the third chapter were used for targets imaging analysis.the results show that the proposed method ensure the array structure is simple,the advantages of hardware implementation,and can suppress the grating lobe and false target,improve the image quality.In addition,compared with SBRIM algorithm,combined the imaging algorithm proposed in chapter 3 with the proposed method not only have the advantages above but also improve the algorithm efficiency significantly.