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Article: Adaptive Fading Bayesian Unscented Kalman Filter and Smoother for State Estimation of Unmanned Aircraft Systems
Title | Adaptive Fading Bayesian Unscented Kalman Filter and Smoother for State Estimation of Unmanned Aircraft Systems |
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Authors | |
Keywords | Bayesian smoothing nonlinear and non-Gaussian system Gaussian mixture unmanned aircraft systems unscented Kalman filter |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639 |
Citation | IEEE Access, 2020, v. 8, p. 119470-119486 How to Cite? |
Abstract | This paper proposes an adaptive fading Bayesian unscented Kalman filter (AF-BUKF) and explores its application for state estimation of unmanned aircraft systems (UASs). In the AF-BUKF, the state and noise densities are approximated as finite Gaussian mixtures, in which the mean and covariance for each component are recursively estimated using the UKF. To avoid the prohibitive computational complexity caused by the exponential growth of mixture components, a Gaussian mixture simplification algorithm is employed. Moreover, the AF-BUKF algorithm employs a novel adaptive fading strategy to recursively update the Gaussian components, so that the adverse effect of inexact knowledge of the state and measurement noise covariance can be mitigated. An AF-BUK Smoother (AF-BUKS) is also proposed by extending the AF-BUKF algorithm using the concept of optimal Bayesian smoothing and the Rauch-Tung-Striebel Smoother to improve estimation accuracy. Experimental results on simulated and real UAS data show that the proposed AF-BUKF/S algorithms can achieve better performance compared with the conventional methods. Thus, they can serve as attractive alternative approaches for nonlinear state estimation of UASs and other problems. |
Persistent Identifier | http://hdl.handle.net/10722/294069 |
ISSN | 2023 Impact Factor: 3.4 2023 SCImago Journal Rankings: 0.960 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | LIU, Z | - |
dc.contributor.author | Chan, SC | - |
dc.date.accessioned | 2020-11-23T08:25:53Z | - |
dc.date.available | 2020-11-23T08:25:53Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Access, 2020, v. 8, p. 119470-119486 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10722/294069 | - |
dc.description.abstract | This paper proposes an adaptive fading Bayesian unscented Kalman filter (AF-BUKF) and explores its application for state estimation of unmanned aircraft systems (UASs). In the AF-BUKF, the state and noise densities are approximated as finite Gaussian mixtures, in which the mean and covariance for each component are recursively estimated using the UKF. To avoid the prohibitive computational complexity caused by the exponential growth of mixture components, a Gaussian mixture simplification algorithm is employed. Moreover, the AF-BUKF algorithm employs a novel adaptive fading strategy to recursively update the Gaussian components, so that the adverse effect of inexact knowledge of the state and measurement noise covariance can be mitigated. An AF-BUK Smoother (AF-BUKS) is also proposed by extending the AF-BUKF algorithm using the concept of optimal Bayesian smoothing and the Rauch-Tung-Striebel Smoother to improve estimation accuracy. Experimental results on simulated and real UAS data show that the proposed AF-BUKF/S algorithms can achieve better performance compared with the conventional methods. Thus, they can serve as attractive alternative approaches for nonlinear state estimation of UASs and other problems. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639 | - |
dc.relation.ispartof | IEEE Access | - |
dc.rights | IEEE Access. Copyright © Institute of Electrical and Electronics Engineers (IEEE): OAJ. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Bayesian smoothing | - |
dc.subject | nonlinear and non-Gaussian system | - |
dc.subject | Gaussian mixture | - |
dc.subject | unmanned aircraft systems | - |
dc.subject | unscented Kalman filter | - |
dc.title | Adaptive Fading Bayesian Unscented Kalman Filter and Smoother for State Estimation of Unmanned Aircraft Systems | - |
dc.type | Article | - |
dc.identifier.email | Chan, SC: scchan@eee.hku.hk | - |
dc.identifier.authority | Chan, SC=rp00094 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3004804 | - |
dc.identifier.scopus | eid_2-s2.0-85088295606 | - |
dc.identifier.hkuros | 319276 | - |
dc.identifier.volume | 8 | - |
dc.identifier.spage | 119470 | - |
dc.identifier.epage | 119486 | - |
dc.identifier.isi | WOS:000552005600001 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2169-3536 | - |