A distributed local Kalman consensus filter for traffic estimation

Ye Sun, Daniel B. Work

Research output: Contribution to journalConference article

Abstract

This work proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) is used to describe the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, we prove that the estimates given by the DLKCF are ultimately bounded. Numerical experiments are provided to show the asymptotic stability of the DLKCF for observable modes, and illustrate the effect of the DLKCF on promoting consensus among various local agents. Supplementary source code is available at https://github.com/yesun/DLKCFcdc2014.

Original languageEnglish (US)
Article number7040406
Pages (from-to)6484-6491
Number of pages8
JournalProceedings of the IEEE Conference on Decision and Control
Volume2015-February
Issue numberFebruary
DOIs
StatePublished - Jan 1 2014
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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