Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145323
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dc.contributor.authorZhang, Jiaqien_US
dc.contributor.authorYou, Keyouen_US
dc.contributor.authorXie, Lihuaen_US
dc.date.accessioned2020-12-17T05:14:32Z-
dc.date.available2020-12-17T05:14:32Z-
dc.date.issued2018-
dc.identifier.citationZhang, J., You, K., & Xie, L. (2019). Bayesian Filtering With Unknown Sensor Measurement Losses. IEEE Transactions on Control of Network Systems, 6(1), 163–175. doi:10.1109/tcns.2018.2802872en_US
dc.identifier.issn2325-5870en_US
dc.identifier.urihttps://hdl.handle.net/10356/145323-
dc.description.abstractThis paper studies the state estimation problem of a stochastic nonlinear system with unknown sensor measurement losses. If the estimator knows the sensor measurement losses of a linear Gaussian system, the minimum variance estimate is easily computed by the celebrated intermittent Kalman filter (IKF). However, this will no longer be the case when the measurement losses are unknown and/or the system is nonlinear or non-Gaussian. By exploiting the binary property of the measurement loss process and the IKF, we design three suboptimal filters for the state estimation, that is, BKF-I, BKF-II, and RBPF. The BKF-I is based on the MAP estimator of the measurement loss process and the BKF-II is derived by estimating the conditional loss probability. The RBPF is a particle filter-based algorithm that marginalizes out the loss process to increase the efficiency of particles. All of the proposed filters can be easily implemented in recursive forms. Finally, a linear system, a target tracking system, and a quadrotor's path control problem are included to illustrate their effectiveness, and show the tradeoff between computational complexity and estimation accuracy of the proposed filters.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationRG78/15en_US
dc.relation.ispartofIEEE Transactions on Control of Network Systemsen_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCNS.2018.2802872.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleBayesian filtering with unknown sensor measurement lossesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/TCNS.2018.2802872-
dc.description.versionAccepted versionen_US
dc.identifier.issue1en_US
dc.identifier.volume6en_US
dc.identifier.spage163en_US
dc.identifier.epage175en_US
dc.subject.keywordsIntermittent Kalman filter (IKF)en_US
dc.subject.keywordsNetworked Estimationen_US
dc.description.acknowledgementThis work was supported in partby the National Key Research and Development Program of China un-der Grant 2017YFC0805310, in part by the National Natural ScienceFoundation of China under Grant 61722308 and Grant 41427806, andin part by the Ministry of Education of Singapore under Grant MoE Tier RG78/15.en_US
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