• DocumentCode
    620606
  • Title

    The UIO-based LMMSE filter for NCSs with packet dropout

  • Author

    Hang Geng ; Yan Liang ; Xiaojing Zhang

  • Author_Institution
    Sch. of Electron., Northwestern Polytech. Univ., Xi´an, China
  • fYear
    2013
  • fDate
    25-27 May 2013
  • Firstpage
    4978
  • Lastpage
    4983
  • Abstract
    This paper puts forward a UIO-based linear-minimum-mean-square-error (LMMSE) estimation problem of a class of the net-worked control systems (NCSs), and the disturbance of the state and its measurements is zero mean white noise with covariance being ; An unknown input observer (UIO) is established stemmed from a linear multi-sensor model and a linear time-varying estimation error system is proposed where packet dropouts are presented as zero-mean white input noise. Based on the error system, a LMMSE estimator is set up to obtain an error estimate of the state. By compensating the primary estimate of the UIO with the error estimate of the LMMSE, a much more accurate estimate of the state is achieved. A numerical example of distributive target tracking is given to illustrate the proposed estimator.
  • Keywords
    filtering theory; linear systems; mean square error methods; networked control systems; observers; sensor fusion; state estimation; statistical analysis; time-varying systems; white noise; LMMSE estimator; NCS; UIO-based LMMSE filter; covariance; distributive target tracking; linear minimum mean square error; linear multisensor model; linear time-varying estimation error system; networked control system; packet dropout; state error estimation; unknown input observer; zero-mean white input noise; Computational modeling; Covariance matrices; Estimation error; Measurement uncertainty; Observers; Target tracking; Linear-minimum-mean-square-error filter; Multi-sensor Fusion; Networked control systems; Packet dropout; Unknown input observers;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (CCDC), 2013 25th Chinese
  • Conference_Location
    Guiyang
  • Print_ISBN
    978-1-4673-5533-9
  • Type

    conf

  • DOI
    10.1109/CCDC.2013.6561835
  • Filename
    6561835