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Distributed Kalman filtering fusion with packet loss or intermittent communications from local estimators to fusion center

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Abstract

This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to fusion center while the process noise does exist. When the local estimates are not lost too much, the authors propose an optimal distributed fusion algorithm which is equivalent to the corresponding centralized Kalman filtering fusion with complete communications even if the process noise does exist. When this condition is not satisfied, based on the above global optimality result and sensor data compression, the authors propose a suboptimal distributed fusion algorithm. Numerical examples show that this suboptimal algorithm still works well and significantly better than the standard distributed Kalman filtering fusion subject to packet loss even if the process noise power is quite large.

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Authors and Affiliations

  1. College of Mathematics, Sichuan University, Chengdu, 610064, China

    Yingting Luo, Yunmin Zhu, Xiaojing Shen & Enbin Song

Authors
  1. Yingting Luo
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  2. Yunmin Zhu
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  3. Xiaojing Shen
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  4. Enbin Song
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Corresponding author

Correspondence to Yingting Luo.

Additional information

This research is supported by the National Natural Science Foundation of China under Grant Nos. 60934009, 60901037 and 61004138.

This paper was recommended for publication by Editor Jifeng ZHANG.

In this paper, we call the estimate is globally optimal means that the estimate result is equal to the linear minimum mean square error estimate using all sensor measurements from up to now. Otherwise, we call the estimate is suboptimal.

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Luo, Y., Zhu, Y., Shen, X. et al. Distributed Kalman filtering fusion with packet loss or intermittent communications from local estimators to fusion center. J Syst Sci Complex 25, 463–485 (2012). https://doi.org/10.1007/s11424-012-0275-2

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  • DOI: https://doi.org/10.1007/s11424-012-0275-2

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