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An Efficient Non-linear Kalman Filtering Algorithm Using Simultaneous Perturbation and Applications in Traffic Estimation and Prediction
Northeastern University.
2007 (English)In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2007, 217-222 p.Conference paper (Refereed)
Abstract [en]

The Extended Kalman Filter, a well-established and straightforward extension of theKalman filter, requires a computationally intensive linearization step. In this paper, the use of the simultaneous perturbation is proposed for the computation of the gradient in a far more efficient way than the usual numerical derivatives. The resulting algorithm is applied to the problem of on-line calibration of traffic dynamics models and empirical results are presented. The use of the simultaneous perturbation gradient approximation provides significant improvement over the base case, and comparable results to those obtained by the more computationally intensive finite difference gradient approximation. 

Place, publisher, year, edition, pages
2007. 217-222 p.
Keyword [en]
Empirical results, Finite difference, Gradient approximation, Intelligent transportation systems, Kalman Filtering algorithms, Non-linear, Numerical derivatives, On-line calibrations, Simultaneous perturbation, Traffic dynamics, Traffic estimation
National Category
Transport Systems and Logistics
URN: urn:nbn:se:kth:diva-88165DOI: 10.1109/ITSC.2007.4357813ISI: 000253972100189ISBN: 978-142441396-6OAI: diva2:502154
10th Intelligent Transportation Systems Conference, Seattle
TSC import 1994 2012-02-14. QC 20120228Available from: 2012-02-14 Created: 2012-02-14 Last updated: 2012-02-28Bibliographically approved

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Koutsopoulos, Haris
Transport Systems and Logistics

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ReferencesLink to record
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