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Distributed Motion State Estimation of Mobile Target with Switching Topologies

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Abstract

For the problem of motion state estimation of mobile target tracked by sensor nodes, the information-weighted Kalman consensus filter (IKCF) is introduced for sensor networks with switching communication topologies. In order to improve the dynamic performance of the IKCF, the low-pass consensus filtering algorithm is used to estimate the motion acceleration of the target. Moreover, an improved low-pass consensus filter is proposed to make the estimated motion accelerations converge to a smaller range. With low-pass consensus filtering for measured motion accelerations, the switched linear system of collective estimation errors of the IKCF is derived. Furthermore, it is proved that the switched linear system of collective estimation errors is globally uniformly asymptotically stable with a weighted \({l_2}\)-gain. Consequently, the conclusion is deduced that estimations of the target motion state can achieve consensus and converge to the target motion state within a bounded region as \(t \rightarrow \infty \) under switching topologies. Finally, the effectiveness of the proposed approaches is illustrated by several illustrative examples.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (61703286).

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Authors and Affiliations

  1. School of Automation, Shenyang Aerospace University, Shenyang, 110136, China

    Huaqing Zhang, Hongmei Zhang, Hao Liu & Guangyan Xu

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  1. Huaqing Zhang
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  2. Hongmei Zhang
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  3. Hao Liu
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  4. Guangyan Xu
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Correspondence to Hongmei Zhang.

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Zhang, H., Zhang, H., Liu, H. et al. Distributed Motion State Estimation of Mobile Target with Switching Topologies. Circuits Syst Signal Process 39, 2648–2672 (2020). https://doi.org/10.1007/s00034-019-01282-z

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