DocumentCode :
2652888
Title :
A derivative-free nonlinear algorithm for speed estimation using data from single loop detectors
Author :
Zhang, Yunlong ; Ye, Zhirui
Author_Institution :
Zachry Dept. of Civil Eng., Texas A&M Univ., College Station, TX
fYear :
2006
fDate :
17-20 Sept. 2006
Firstpage :
1035
Lastpage :
1040
Abstract :
This paper presents a derivative-free algorithm for speed estimation using occupancy and count outputs from single loop detectors. An unscented Kalman filter (UKF) is used for the nonlinear speed estimation problem and achieved excellent results. Data from a Texas Transportation Institute (TTI) vehicle detector test bed are used for the implementation of the UKF and also for performance evaluation of the implemented algorithm. The results showed that the UKF method has superior performance to other prior methods
Keywords :
Kalman filters; sensors; transportation; velocity measurement; derivative-free nonlinear algorithm; nonlinear speed estimation; single loop detector; unscented Kalman filter; Civil engineering; Detectors; Intelligent transportation systems; Monitoring; Nonlinear systems; Road transportation; Surveillance; Testing; Vehicle detection; Velocity measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE
Conference_Location :
Toronto, Ont.
Print_ISBN :
1-4244-0093-7
Electronic_ISBN :
1-4244-0094-5
Type :
conf
DOI :
10.1109/ITSC.2006.1707358
Filename :
1707358
Link To Document :
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