DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Ju Hong | ko |
dc.contributor.author | Kim, Du Yong | ko |
dc.contributor.author | Yoon, Kuk-Jin | ko |
dc.date.accessioned | 2018-03-21T02:56:06Z | - |
dc.date.available | 2018-03-21T02:56:06Z | - |
dc.date.created | 2018-03-12 | - |
dc.date.created | 2018-03-12 | - |
dc.date.created | 2018-03-12 | - |
dc.date.issued | 2013-09 | - |
dc.identifier.citation | SIGNAL PROCESSING, v.93, no.9, pp.2664 - 2670 | - |
dc.identifier.issn | 0165-1684 | - |
dc.identifier.uri | http://hdl.handle.net/10203/240808 | - |
dc.description.abstract | The unscented sequential Monte Carlo probability hypothesis density (USMC-PHD) filter has been proposed to improve the accuracy performance of the bootstrap SMC-PHD filter in cluttered environments. However, the USMC-PHD filter suffers from heavy computational complexity because the unscented information filter is assigned for every particle to approximate an importance sampling function. In this paper, we propose a Gaussian mixture form of the importance sampling function for the SMC-PHD filter to considerably reduce the computational complexity without performance degradation. Simulation results support that the proposed importance sampling function is effective in computational aspects compared with variants of SMC-PHD filters and competitive to the USMC-PHD filter in accuracy. (c) 2013 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | HYPOTHESIS DENSITY FILTER | - |
dc.subject | RANDOM FINITE SETS | - |
dc.subject | TRACKING | - |
dc.subject | INFORMATION | - |
dc.title | Gaussian mixture importance sampling function for unscented SMC-PHD filter | - |
dc.type | Article | - |
dc.identifier.wosid | 000320347600030 | - |
dc.identifier.scopusid | 2-s2.0-84877950189 | - |
dc.type.rims | ART | - |
dc.citation.volume | 93 | - |
dc.citation.issue | 9 | - |
dc.citation.beginningpage | 2664 | - |
dc.citation.endingpage | 2670 | - |
dc.citation.publicationname | SIGNAL PROCESSING | - |
dc.identifier.doi | 10.1016/j.sigpro.2013.03.004 | - |
dc.contributor.localauthor | Yoon, Kuk-Jin | - |
dc.contributor.nonIdAuthor | Yoon, Ju Hong | - |
dc.contributor.nonIdAuthor | Kim, Du Yong | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Multitarget filtering | - |
dc.subject.keywordAuthor | Probability hypothesis density (PHD) filter | - |
dc.subject.keywordAuthor | Importance sampling function | - |
dc.subject.keywordAuthor | Sequential Monte Carlo | - |
dc.subject.keywordAuthor | Gaussian mixture | - |
dc.subject.keywordAuthor | Multitarget filtering | - |
dc.subject.keywordAuthor | Probability hypothesis density (PHD) filter | - |
dc.subject.keywordAuthor | Importance sampling function | - |
dc.subject.keywordAuthor | Sequential Monte Carlo | - |
dc.subject.keywordAuthor | Gaussian mixture | - |
dc.subject.keywordPlus | HYPOTHESIS DENSITY FILTER | - |
dc.subject.keywordPlus | RANDOM FINITE SETS | - |
dc.subject.keywordPlus | TRACKING | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordPlus | HYPOTHESIS DENSITY FILTER | - |
dc.subject.keywordPlus | RANDOM FINITE SETS | - |
dc.subject.keywordPlus | TRACKING | - |
dc.subject.keywordPlus | INFORMATION | - |
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