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Sequential fusion estimation for multisensor systems with non-Gaussian noises

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Abstract

The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavy-tailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter, the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.

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Acknowledgements

This work was supported by Beijing Natural Science Foundation (Grant No. 4202071).

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Authors and Affiliations

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

    Liping Yan, Chenying Di & Yuanqing Xia

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

    Liping Yan & Q. M. Jonathan Wu

Authors
  1. Liping Yan
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  2. Chenying Di
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  3. Q. M. Jonathan Wu
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  4. Yuanqing Xia
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Correspondence to Liping Yan.

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Yan, L., Di, C., Wu, Q.M.J. et al. Sequential fusion estimation for multisensor systems with non-Gaussian noises. Sci. China Inf. Sci. 63, 222202 (2020). https://doi.org/10.1007/s11432-019-2725-8

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  • DOI: https://doi.org/10.1007/s11432-019-2725-8

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