Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145811
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dc.contributor.authorZhang, Ranen_US
dc.contributor.authorLiu, Zichuanen_US
dc.contributor.authorQian, Kemaoen_US
dc.contributor.authorZhang, Shengen_US
dc.contributor.authorDu, Pengfeien_US
dc.contributor.authorChen, Chenen_US
dc.contributor.authorAlphones, Arokiaswamien_US
dc.date.accessioned2021-01-08T08:33:26Z-
dc.date.available2021-01-08T08:33:26Z-
dc.date.issued2020-
dc.identifier.citationZhang, R., Liu, Z., Qian, K., Zhang, S., Du, P., Chen, C., & Alphones, A. (2020). Outage bridging and trajectory recovery in visible light positioning using insufficient RSS information. IEEE Access, 8, 162302-162312. doi:10.1109/ACCESS.2020.3020874en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttps://hdl.handle.net/10356/145811-
dc.description.abstractIndoor positioning technology is vital for various location-aware applications while visible light positioning (VLP) is especially promising due to its ubiquitous and energy-efficient features. VLP has been widely investigated under the assumption of line of sight (LoS), yet, VLP signal blockage can happen frequently in a practical indoor environment and brings about outage problems to indoor localization/tracking services. However, this problem is usually overlooked or sidestepped in the existing works. Our work, for the first time, investigates the outage problem in a received signal strength (RSS)-based VLP system. Efficient algorithms for outage bridging and trajectory recovery are proposed by smartly fusing with insufficient RSS information. Specifically, a partial-RSS-assisted inertial navigation system (PRAINS) inspired by extended Kalman filter (EKF) is developed to bridge sporadic outage, while a bi-directional structured PRAINS (Bid-PRAINS) is developed to use both pre- and post- outage information to recover the lost trajectory information. To further deal with a more general situation when the system noise features are not pre-known and hard to be measured/estimated, a semi-parameterized RNN based learnable Kalman filter (SPR-LKF) is proposed in place of the EKF to learn the observation/transition noise features and optimize the estimation simultaneously through a recurrent neural network (RNN). Extensive tests show that the PRAINS/ Bid-PRAINS has at least 62% accuracy improvement over the conventional inertial navigation system (INS)-only algorithm, while the proposed SPR-LKF/ Bid-SPR-LKF can offer an even better accuracy gain of 70% even without pre-knowing the system noise feature.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Accessen_US
dc.rights© 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleOutage bridging and trajectory recovery in visible light positioning using insufficient RSS informationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/ACCESS.2020.3020874-
dc.description.versionPublished versionen_US
dc.identifier.volume8en_US
dc.identifier.spage162302en_US
dc.identifier.epage162312en_US
dc.subject.keywordsVisible Light Positioningen_US
dc.subject.keywordsRecurrent Neural Networken_US
dc.description.acknowledgementThis work was conducted within the Delta-Nanyang Technological University (NTU) Corporate Lab for Cyber-Physical Systems with funding support from Delta Electronics SInc. and the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme.en_US
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