Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot Localization
DC Field | Value | Language |
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dc.contributor.author | Kim, Young Eun | - |
dc.contributor.author | Kang, Hyun Ho | - |
dc.contributor.author | Ahn, Choon Ki | - |
dc.date.accessioned | 2021-08-31T16:02:53Z | - |
dc.date.available | 2021-08-31T16:02:53Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/58969 | - |
dc.description.abstract | In this paper, we propose a new state estimator called the two-layer nonlinear finite impulse response (TLNF) filter and adopt this new filter and unscented Kalman filter (UKF) as subfilters to create the fusion TLNF/UK filter. The TLNF filter is constructed with measurements that are redefined by weighting the estimated states acquired through minimizing the cost function based on the Frobenius norm. The efficient iterative form of the TLNF filter is also developed in this paper. Using the fact that the UKF and the TLNF filter each takes a different type of memory structure, the fusion TLNF/UK filter is designed as a robust nonlinear state estimator taking both advantages of each filter. To obtain the best fusion estimates, probabilistic weights are computed based on Bayes & x2019; rule and the likelihood of each filter. Both simulation and experimental results for mobile robot indoor localization have shown that the fusion TLNF/UK filter achieves a higher level of accuracy and robustness under practical situations. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | FROBENIUS NORM | - |
dc.subject | IGNORING NOISE | - |
dc.title | Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot Localization | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2992695 | - |
dc.identifier.scopusid | 2-s2.0-85085259624 | - |
dc.identifier.wosid | 000538765600107 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.87173 - 87183 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.citation.startPage | 87173 | - |
dc.citation.endPage | 87183 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | FROBENIUS NORM | - |
dc.subject.keywordPlus | IGNORING NOISE | - |
dc.subject.keywordAuthor | State estimation | - |
dc.subject.keywordAuthor | fusion algorithm | - |
dc.subject.keywordAuthor | finite impulse response (FIR) filter | - |
dc.subject.keywordAuthor | unscented Kalman filter (UKF) | - |
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