In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource man...In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource management and deformation observation.Fucheng-1 is the first C-band commercial SAR satellite for interferometric SAR(InSAR)service developed by Spacety China,which marks the gradual maturity of China’s remote sensing data service.Based on the raw data collected by Fucheng-1,this paper firstly introduces the range-Doppler algorithm(RDA),then illustrates the parameter estimation method on the basis of fractional Fourier transform(FrFT)to realize the accurate estimation of azimuth chirp rate,which effectively improves imaging quality.Finally,the L1-norm regularization based sparse imaging method is utilized to reconstruct images from down-sampled data.Experimental results show that the sparse imaging algorithm can accurately reconstruct the down-sampled Fucheng-1 data and suppress sidelobes and clutter.展开更多
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
A multiple-input multiple-output（MIMO） sonar can synthesize a large-aperture virtual uniform linear array（ULA） from a small number of physical elements. However, the large aperture is obtained at the cost of a gre...A multiple-input multiple-output（MIMO） sonar can synthesize a large-aperture virtual uniform linear array（ULA） from a small number of physical elements. However, the large aperture is obtained at the cost of a great number of matched filters with much heavy computation load. To reduce the computation load, a MIMO sonar imaging method using a virtual sparse linear array（SLA） is proposed, which contains the offline and online processing. In the offline processing, the virtual ULA of the MIMO sonar is thinned to a virtual SLA by the simulated annealing algorithm, and matched filters corresponding to inactive virtual elements are removed. In the online processing, outputs of matched filters corresponding to active elements are collected for further multibeam processing and hence, the number of matched filters in the echo processing procedure is effectively reduced. Numerical simulations show that the proposed method can reduce the computation load effectively while obtaining a similar imaging performance as the traditional method.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
Sparse signal processing is a signal processing technique that takes advantage of signal’s sparsity,allowing signal to be recovered with a reduced number of samples.Compressive sensing,a new branch of the sparse sign...Sparse signal processing is a signal processing technique that takes advantage of signal’s sparsity,allowing signal to be recovered with a reduced number of samples.Compressive sensing,a new branch of the sparse signal processing,has become a rapidly growing research field.Sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theories,new systems and new methodologies of microwave imaging.This paper first summarizes the latest application of sparse microwave imaging,including Synthetic Aperture Radar(SAR),tomographic SAR and inverse SAR.As sparse signal processing keeps evolving,an avalanche of results have been obtained.We also highlight its recent theoretical advances,including structured sparsity,off-grid,Bayesian approaches,and point out new research directions in sparse microwave imaging.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 t...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
基金supported in part by the National Natural Science Foundation of China(No.62271248)the Natural Science Foundation of Jiangsu Province(No.BK20230090)the Key Laboratory of Land Satellite Remote Sensing Application through the Ministry of Natural Resources of China(No.KLSMNR-K202303).
文摘In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource management and deformation observation.Fucheng-1 is the first C-band commercial SAR satellite for interferometric SAR(InSAR)service developed by Spacety China,which marks the gradual maturity of China’s remote sensing data service.Based on the raw data collected by Fucheng-1,this paper firstly introduces the range-Doppler algorithm(RDA),then illustrates the parameter estimation method on the basis of fractional Fourier transform(FrFT)to realize the accurate estimation of azimuth chirp rate,which effectively improves imaging quality.Finally,the L1-norm regularization based sparse imaging method is utilized to reconstruct images from down-sampled data.Experimental results show that the sparse imaging algorithm can accurately reconstruct the down-sampled Fucheng-1 data and suppress sidelobes and clutter.
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(51509204)the Opening Project of State Key Laboratory of Acoustics(SKLA201501)the Fundamental Research Funds for the Central Universities(3102015ZY011)
文摘A multiple-input multiple-output（MIMO） sonar can synthesize a large-aperture virtual uniform linear array（ULA） from a small number of physical elements. However, the large aperture is obtained at the cost of a great number of matched filters with much heavy computation load. To reduce the computation load, a MIMO sonar imaging method using a virtual sparse linear array（SLA） is proposed, which contains the offline and online processing. In the offline processing, the virtual ULA of the MIMO sonar is thinned to a virtual SLA by the simulated annealing algorithm, and matched filters corresponding to inactive virtual elements are removed. In the online processing, outputs of matched filters corresponding to active elements are collected for further multibeam processing and hence, the number of matched filters in the echo processing procedure is effectively reduced. Numerical simulations show that the proposed method can reduce the computation load effectively while obtaining a similar imaging performance as the traditional method.
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.
基金supported by the National Basic Research Program of China("973" Project)(Grant No.2010CB731900)
文摘Sparse signal processing is a signal processing technique that takes advantage of signal’s sparsity,allowing signal to be recovered with a reduced number of samples.Compressive sensing,a new branch of the sparse signal processing,has become a rapidly growing research field.Sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theories,new systems and new methodologies of microwave imaging.This paper first summarizes the latest application of sparse microwave imaging,including Synthetic Aperture Radar(SAR),tomographic SAR and inverse SAR.As sparse signal processing keeps evolving,an avalanche of results have been obtained.We also highlight its recent theoretical advances,including structured sparsity,off-grid,Bayesian approaches,and point out new research directions in sparse microwave imaging.
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.
