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Sequential fusion estimation for Markov jump systems with heavy-tailed noises

Author

Listed:
  • Hui Li
  • Liping Yan
  • Yuqin Zhou
  • Yuanqing Xia
  • Xiaodi Shi

Abstract

We study a sequential fusion estimation problem for Markov jump multi-sensor systems with heavy-tailed noises. By modelling the noises as Student's t distributions, a sequential fusion estimation algorithm is designed by utilising the interacting multiple model method and Bayes' rule. To improve the robustness against measurement outliers caused by measurement heavy-tailed noise, an F-distribution detection strategy is designed to detect and reject the measurement outliers. Simulation results demonstrate that the designed sequential fusion estimation algorithm can effectively fuse the measurements from multiple sensors, and the accuracy of the designed algorithm is superior to the existing interacting multiple model Student's t batch fusion algorithm and single model adaptive Student's t batch fusion algorithm when there exist model switching and disturbances with heavy-tailed property.

Suggested Citation

  • Hui Li & Liping Yan & Yuqin Zhou & Yuanqing Xia & Xiaodi Shi, 2023. "Sequential fusion estimation for Markov jump systems with heavy-tailed noises," International Journal of Systems Science, Taylor & Francis Journals, vol. 54(9), pages 1910-1925, July.
    • Handle: RePEc:taf:tsysxx:v:54:y:2023:i:9:p:1910-1925
    DOI: 10.1080/00207721.2023.2210145
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    Cited by:

    1. Li, Qiang & Liang, Jinling & Gong, Weiqiang & Wang, Kai & Wang, Jinling, 2024. "Nonfragile state estimation for semi-Markovian switching CVNs with general uncertain transition rates: An event-triggered scheme," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 204-222.

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