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Title:A distributed local Kalman consensus filter for traffic estimation: design, analysis and validation
Author(s):Sun, Ye
Advisor(s):Work, Daniel B.
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Traffic state estimation
hybrid systems
observability
distributed Kalman filter
consensus filter
Abstract:This thesis proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) describes 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, the estimates given by the DLKCF are proved to be ultimately bounded. We also show that under some frequently encountered conditions, the error sum in an unobservable section converges to a fixed value. Numerical experiments verify the asymptotic stability of the DLKCF for observable modes, compare the DLKCF to a Luenberger observer, illustrate the capability of the DLKCF on promoting consensus among various local agents, and show a considerable reduction of the runtime of the DLKCF compared to a central KF. Supplementary source code is available to be downloaded at https://github.com/yesun/DLKCFthesis.
Issue Date:2015-01-21
URI:http://hdl.handle.net/2142/72800
Rights Information:Copyright 2014 Ye Sun
Date Available in IDEALS:2015-01-21
Date Deposited:2014-12


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