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Multisensor fusion estimation of nonlinear systems with intermittent observations and heavy-tailed noises

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Abstract

Inspired by the robust student t-distribution based nonlinear filter (RSTNF), a student t-distribution and unscented transform (UT) based filter for state estimation of heavy-tailed nonlinear dynamic systems, a modified RSTNF for intermittent observations is derived. The fusion estimation for nonlinear multisensor systems with intermittent observations and heavy-tailed measurement and process noises is studied. In this work, the centralized fusion, the sequential fusion, and the naïve distributed fusion algorithms are presented, respectively. Theoretical analysis shows that the presented algorithms are effective, which are the efficient extension of the classical unscented Kalman filter (UKF) or the cubature Kalman filter (CKF) based algorithms with Gaussian noises. Simulation results show that the presented algorithms are effective and feasible.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62076031, 62073036) and Beijing Natural Science Foundation (Grant No. 4202071).

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Bo Xiao

  2. Department of Electrical and Computer Engineering, University of Windsor, Windsor, N9B3P4, Canada

    Q. M. Jonathan Wu

  3. Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Liping Yan

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  1. Bo Xiao
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  2. Q. M. Jonathan Wu
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  3. Liping Yan
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Correspondence to Bo Xiao.

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Xiao, B., Wu, Q.M.J. & Yan, L. Multisensor fusion estimation of nonlinear systems with intermittent observations and heavy-tailed noises. Sci. China Inf. Sci. 65, 192203 (2022). https://doi.org/10.1007/s11432-020-3223-6

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  • DOI: https://doi.org/10.1007/s11432-020-3223-6

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