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 language | English (US) |
|---|---|
| Article number | 7040406 |
| Pages (from-to) | 6484-6491 |
| Number of pages | 8 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 2015-February |
| Issue number | February |
| DOIs | |
| State | Published - Jan 1 2014 |
| Event | 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States Duration: Dec 15 2014 → Dec 17 2014 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization
Cite this
A distributed local Kalman consensus filter for traffic estimation. / Sun, Ye; Work, Daniel B.
In: Proceedings of the IEEE Conference on Decision and Control, Vol. 2015-February, No. February, 7040406, 01.01.2014, p. 6484-6491.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - A distributed local Kalman consensus filter for traffic estimation
AU - Sun, Ye
AU - Work, Daniel B.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84961991063&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961991063&partnerID=8YFLogxK
U2 - 10.1109/CDC.2014.7040406
DO - 10.1109/CDC.2014.7040406
M3 - Conference article
AN - SCOPUS:84961991063
VL - 2015-February
SP - 6484
EP - 6491
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
SN - 0191-2216
IS - February
M1 - 7040406
ER -