Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/120527
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dc.contributor.authorSudan Hanen_US
dc.contributor.authorChongyi Fanen_US
dc.contributor.authorXiaotao Huangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T06:55:00Z-
dc.date.available2020-04-06T06:55:00Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/LGRS.2016.2635104en_US
dc.identifier.urihttp://localhost/handle/Hannan/120527-
dc.description.abstractSpace-time adaptive processing based on clutter sparse recovery (SR-STAP) methods outperform traditional statistical-STAP algorithms in scenarios with limited training numbers. However, the computational burden of current SR-STAP methods is extremely heavy, particularly when the number of discretized angle and Doppler grid points is large, which hinders these methods from coming into practical use. This letter proposes a spectrum-aided reduced-dimension SR-STAP method to overcome this issue. The proposed method employs the clutter spectrum estimated by training samples to design the reduced-dimension dictionary. By solving a reduced-dimension sparse recovery problem, the computational load of the proposed method can be reduced significantly while only slightly degrading the performance of clutter suppression and target detection compared with current SR-STAP methods. Numerical experiments using both simulated and measured data validate the effectiveness of the proposed method.en_US
dc.format.extent213,en_US
dc.format.extent217en_US
dc.publisherIEEEen_US
dc.relation.haspart7792596.pdfen_US
dc.titleA Novel STAP Based on Spectrum-Aided Reduced-Dimension Clutter Sparse Recoveryen_US
dc.typeArticleen_US
dc.journal.volume14en_US
dc.journal.issue2en_US
Appears in Collections:2017

Files in This Item:
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7792596.pdf465.94 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSudan Hanen_US
dc.contributor.authorChongyi Fanen_US
dc.contributor.authorXiaotao Huangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T06:55:00Z-
dc.date.available2020-04-06T06:55:00Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/LGRS.2016.2635104en_US
dc.identifier.urihttp://localhost/handle/Hannan/120527-
dc.description.abstractSpace-time adaptive processing based on clutter sparse recovery (SR-STAP) methods outperform traditional statistical-STAP algorithms in scenarios with limited training numbers. However, the computational burden of current SR-STAP methods is extremely heavy, particularly when the number of discretized angle and Doppler grid points is large, which hinders these methods from coming into practical use. This letter proposes a spectrum-aided reduced-dimension SR-STAP method to overcome this issue. The proposed method employs the clutter spectrum estimated by training samples to design the reduced-dimension dictionary. By solving a reduced-dimension sparse recovery problem, the computational load of the proposed method can be reduced significantly while only slightly degrading the performance of clutter suppression and target detection compared with current SR-STAP methods. Numerical experiments using both simulated and measured data validate the effectiveness of the proposed method.en_US
dc.format.extent213,en_US
dc.format.extent217en_US
dc.publisherIEEEen_US
dc.relation.haspart7792596.pdfen_US
dc.titleA Novel STAP Based on Spectrum-Aided Reduced-Dimension Clutter Sparse Recoveryen_US
dc.typeArticleen_US
dc.journal.volume14en_US
dc.journal.issue2en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7792596.pdf465.94 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSudan Hanen_US
dc.contributor.authorChongyi Fanen_US
dc.contributor.authorXiaotao Huangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T06:55:00Z-
dc.date.available2020-04-06T06:55:00Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/LGRS.2016.2635104en_US
dc.identifier.urihttp://localhost/handle/Hannan/120527-
dc.description.abstractSpace-time adaptive processing based on clutter sparse recovery (SR-STAP) methods outperform traditional statistical-STAP algorithms in scenarios with limited training numbers. However, the computational burden of current SR-STAP methods is extremely heavy, particularly when the number of discretized angle and Doppler grid points is large, which hinders these methods from coming into practical use. This letter proposes a spectrum-aided reduced-dimension SR-STAP method to overcome this issue. The proposed method employs the clutter spectrum estimated by training samples to design the reduced-dimension dictionary. By solving a reduced-dimension sparse recovery problem, the computational load of the proposed method can be reduced significantly while only slightly degrading the performance of clutter suppression and target detection compared with current SR-STAP methods. Numerical experiments using both simulated and measured data validate the effectiveness of the proposed method.en_US
dc.format.extent213,en_US
dc.format.extent217en_US
dc.publisherIEEEen_US
dc.relation.haspart7792596.pdfen_US
dc.titleA Novel STAP Based on Spectrum-Aided Reduced-Dimension Clutter Sparse Recoveryen_US
dc.typeArticleen_US
dc.journal.volume14en_US
dc.journal.issue2en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7792596.pdf465.94 kBAdobe PDF