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Distributed fusion estimation for multi-sensor asynchronous sampling systems with correlated noises

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  • Honglei Lin
  • Shuli Sun

Abstract

This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor asynchronous sampling systems with correlated noises. The state updates uniformly and the sensors sample randomly. Based on the measurement augmentation method, the asynchronous sampling system is transformed to the synchronous sampling one. Local filter is designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrix between any two local filters is derived. Finally, the optimal distributed fusion filter is proposed by using matrix-weighted fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.

Suggested Citation

  • Honglei Lin & Shuli Sun, 2017. "Distributed fusion estimation for multi-sensor asynchronous sampling systems with correlated noises," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(5), pages 952-960, April.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:5:p:952-960
    DOI: 10.1080/00207721.2016.1224953
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    References listed on IDEAS

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    1. Liping Yan & Bo Xiao & Yuanqing Xia & Mengyin Fu, 2013. "State estimation for a kind of non-uniform sampling dynamic system," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(10), pages 1913-1924.
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    Cited by:

    1. M. De la Sen, 2018. "On Some Sufficiency-Type Stability and Linear State-Feedback Stabilization Conditions for a Class of Multirate Discrete-Time Systems," Mathematics, MDPI, vol. 6(5), pages 1-18, May.

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