For forward-looking array synthetic aperture radar(FASAR),the scattering intensity of ground scatterers fluctuates greatly since there are kinds of vegetations and topography on the surface of the ground,and thus the ...For forward-looking array synthetic aperture radar(FASAR),the scattering intensity of ground scatterers fluctuates greatly since there are kinds of vegetations and topography on the surface of the ground,and thus the signal-to-noise ratio(SNR)of its echo signals corresponding to different vegetations and topography also varies obviously.Owing to the reason known to all,the performance of the sparse reconstruction of compressed sensing(CS)becomes worse in the case of lower SNR,and the quality of the sparse three-dimensional imaging for FASAR would be affected significantly in the practical application.In this paper,the spatial continuity of the ground scatterers is introduced to the sparse recovery algorithm of CS in the threedimensional imaging for FASAR,in which the weighted least square method of the cubic interpolation is used to filter out the bad and isolated scatterer.The simulation results show that the proposed method can realize the sparse three-dimensional imaging of FASAR more effectively in the case of low SNR.展开更多
Aiming at the problem of resource allocation for digital array radar( DAR),a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures tha...Aiming at the problem of resource allocation for digital array radar( DAR),a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.展开更多
Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity insp...Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation(NCSR), in terms of both visual results and quantitative measures.展开更多
基金supported by the National Natural Science Foundation of China(61640006)the Natural Science Foundation of Shannxi Province,China(2019JM-386).
文摘For forward-looking array synthetic aperture radar(FASAR),the scattering intensity of ground scatterers fluctuates greatly since there are kinds of vegetations and topography on the surface of the ground,and thus the signal-to-noise ratio(SNR)of its echo signals corresponding to different vegetations and topography also varies obviously.Owing to the reason known to all,the performance of the sparse reconstruction of compressed sensing(CS)becomes worse in the case of lower SNR,and the quality of the sparse three-dimensional imaging for FASAR would be affected significantly in the practical application.In this paper,the spatial continuity of the ground scatterers is introduced to the sparse recovery algorithm of CS in the threedimensional imaging for FASAR,in which the weighted least square method of the cubic interpolation is used to filter out the bad and isolated scatterer.The simulation results show that the proposed method can realize the sparse three-dimensional imaging of FASAR more effectively in the case of low SNR.
基金Supported by the National Natural Science Foundation of China(61471386)
文摘Aiming at the problem of resource allocation for digital array radar( DAR),a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.
基金supported by the National Natural Science Foundation of China(Nos.61403146 and 61603105)the Fundamental Research Funds for the Central Universities(No.2015ZM128)the Science and Technology Program of Guangzhou in China(Nos.201707010054 and 201704030072)
文摘Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation(NCSR), in terms of both visual results and quantitative measures.
