Recursive filtering for communication-based train control systems with packet dropouts

Tao Wen, Lei Zou, Jinling Liang, Clive Roberts

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
162 Downloads (Pure)

Abstract

Accurate information about the train position and velocity is critically important for Communication-based Train Control (CBTC) systems. However, it is practically difficult to obtain the precise information of such information due mainly to the “inaccurate measurements” induced by the measurement noises and the “unreliable communication” caused by the wireless train-ground communication. In this paper, a recursive filtering algorithm is proposed to generate the estimates of the train position and velocity for CBTC systems subject to the measurement noise and packet dropouts. Firstly, the dynamics of a train is modeled based on the Newton’s motion equation. Then, a Bernoulli distributed sequence is introduced to describe the packet dropout phenomenon of the wireless communication. The purpose of the problem addressed is to design a recursive filter such that there exists an upper bound for the filtering error covaSix Talent Peaks Project for the High Level Personnelriance. Subsequently, such an upper bound is minimized by properly designing the filter parameter recursively. The desired filter parameter is obtained by solving two Riccati-like difference equations that are of a recursive form suitable for online applications. Finally, an illustrative example is given to show the effective of the proposed filter design scheme.
Original languageEnglish
JournalNeurocomputing
Early online date22 Sep 2017
DOIs
Publication statusE-pub ahead of print - 22 Sep 2017

Keywords

  • recursive filtering
  • communication-based train control systems
  • packet dropouts
  • Riccati-like difference equations

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