Nonstationary Hidden Markov Models for Multiaspect Discriminative Feature Extraction From Radar Targets
This paper presents a new scheme for radar target recognition, in which we fuse sequential radar echoes from multiple target-radar aspect angles. The nonstationary hidden Markov model (NSHMM) is employed to characterize the sequential information contained in multiaspect radar echoes. Features from...
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| Veröffentlicht in: | IEEE transactions on signal processing 2007-05, Vol.55 (5), p.2203-2214 |
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| container_end_page | 2214 |
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| container_issue | 5 |
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| container_title | IEEE transactions on signal processing |
| container_volume | 55 |
| creator | Zhu, Feng Zhang, Xian-Da Hu, Ya-Feng Xie, Deguang |
| description | This paper presents a new scheme for radar target recognition, in which we fuse sequential radar echoes from multiple target-radar aspect angles. The nonstationary hidden Markov model (NSHMM) is employed to characterize the sequential information contained in multiaspect radar echoes. Features from echoes are extracted via the multirelax algorithm, and moments are used to reduce the extracted-feature dimensionality. The proposed NSHMM has many parameters and states to be estimated, so the Markov chain Monte Carlo sampling algorithm is adopted. Finally, this new scheme is demonstrated with experiments on inverse synthetic aperture radar data |
| doi_str_mv | 10.1109/TSP.2007.892708 |
| format | Article |
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The nonstationary hidden Markov model (NSHMM) is employed to characterize the sequential information contained in multiaspect radar echoes. Features from echoes are extracted via the multirelax algorithm, and moments are used to reduce the extracted-feature dimensionality. The proposed NSHMM has many parameters and states to be estimated, so the Markov chain Monte Carlo sampling algorithm is adopted. 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(IEEE) 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c296t-681e3b2c091293f707cc662fc6458b72ba25d802213507f3ce5f2d790ef4bc143</citedby><cites>FETCH-LOGICAL-c296t-681e3b2c091293f707cc662fc6458b72ba25d802213507f3ce5f2d790ef4bc143</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4156439$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54737</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4156439$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18712042$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhu, Feng</creatorcontrib><creatorcontrib>Zhang, Xian-Da</creatorcontrib><creatorcontrib>Hu, Ya-Feng</creatorcontrib><creatorcontrib>Xie, Deguang</creatorcontrib><title>Nonstationary Hidden Markov Models for Multiaspect Discriminative Feature Extraction From Radar Targets</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>This paper presents a new scheme for radar target recognition, in which we fuse sequential radar echoes from multiple target-radar aspect angles. The nonstationary hidden Markov model (NSHMM) is employed to characterize the sequential information contained in multiaspect radar echoes. Features from echoes are extracted via the multirelax algorithm, and moments are used to reduce the extracted-feature dimensionality. The proposed NSHMM has many parameters and states to be estimated, so the Markov chain Monte Carlo sampling algorithm is adopted. 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| ispartof | IEEE transactions on signal processing, 2007-05, Vol.55 (5), p.2203-2214 |
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| subjects | Algorithms Applied sciences Computer simulation Data mining Echoes Exact sciences and technology Feature extraction Fuses Hidden Markov models high-range resolution profile (HRRP) Information science Information, signal and communications theory Inverse synthetic aperture radar Markov chain Monte Carlo (MCMC) Mathematical models Miscellaneous Monte Carlo methods nonstationary hidden Markov model (NSHMM) Radar applications Radar echoes radar target recognition Radar targets Sampling Sampling, quantization Signal and communications theory Signal processing State estimation Target recognition Telecommunications and information theory |
| title | Nonstationary Hidden Markov Models for Multiaspect Discriminative Feature Extraction From Radar Targets |
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