A review of Bayes filters with machine learning techniques and their applications

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dc.contributor.authorKim, Sukkeun
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorShin, Hyo-Sang
dc.date.accessioned2024-11-26T16:25:59Z
dc.date.available2024-11-26T16:25:59Z
dc.date.freetoread2024-11-26
dc.date.issued2025-02-01
dc.date.pubOnline2024-10-02
dc.description.abstractA Bayes filter is a widely used estimation algorithm, but it has inherent limitations. Performance can degrade when the dynamics are highly nonlinear or when the probability distribution of the state is unknown. To mitigate these issues, machine learning (ML) techniques have been incorporated into many Bayes filters, due to their advantage of being able to map between the input and the output without explicit instructions. In this review, we reviewed 90 papers that proposed the use of ML techniques with Bayes filters to improve estimation performance. This review provides an overview of Bayes filters with ML techniques, categorised according to the role of ML, remaining challenges and research gaps. In the concluding section of this review, we point out directions for future research.
dc.description.journalNameInformation Fusion
dc.identifier.citationKim S, Petrunin I, Shin H-S. (2025) A review of Bayes filters with machine learning techniques and their applications. Information Fusion, Volume 114, February 2025, Article number 102707
dc.identifier.eissn1872-6305
dc.identifier.elementsID554854
dc.identifier.issn1566-2535
dc.identifier.paperNo102707
dc.identifier.urihttps://doi.org/10.1016/j.inffus.2024.102707
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23222
dc.identifier.volumeNo114
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S1566253524004858?via%3Dihub
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBayes filter
dc.subjectMachine learning
dc.subjectSurvey
dc.subjectReview
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject4603 Computer Vision and Multimedia Computation
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectArtificial Intelligence & Image Processing
dc.subject4602 Artificial intelligence
dc.subject4603 Computer vision and multimedia computation
dc.subject4605 Data management and data science
dc.titleA review of Bayes filters with machine learning techniques and their applications
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-09-14

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