摘要
The sparse recovery algorithms formulate synthetic aperture radar(SAR) imaging problem in terms of sparse representation(SR) of a small number of strong scatters' positions among a much large number of potential scatters' positions,and provide an effective approach to improve the SAR image resolution.Based on the attributed scatter center model,several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques,namely,sparse Bayesian learning(SBL),fast Bayesian matching pursuit(FBMP),smoothed l0 norm method(SL0),sparse reconstruction by separable approximation(SpaRSA),fast iterative shrinkage-thresholding algorithm(FISTA),and the parameter settings in five SR algorithms were discussed.In different situations,the performances of these algorithms were also discussed.Through the comparison of MSE and failure rate in each algorithm simulation,FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model.Although the SBL is time-consuming,it always get better performance when related to failure rate and high SNR.
The sparse recovery algorithms formulate synthetic aperture radar (SAR) imaging problem in terms of sparse representation (SR) of a small number of strong scatters' positions among a much large number of potential scatters' positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning (SBL), fast Bayesian matching pursuit (FBMP), smoothed 10 norm method (SL0), sparse reconstruction by separable approximation (SpaRSA), fast iterative shrinkage-thresholding algorithm (FISTA), and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.
基金
Project(61171133)supported by the National Natural Science Foundation of China
Project(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,China
Project(61101182)supported by National Natural Science Foundation for Young Scientists of China
关键词
合成孔径雷达
散射模型
稀疏表示
雷达成像
恢复技术
基础
归因
贝叶斯学习
attributed scatter center model
sparse representation
sparse Bayesian learning
fast Bayesian matching pursuit
smoothed l0 norm
sparse reconstruction by separable approximation
fast iterative shrinkage-thresholding algorithm