Abstract
Kalman filter is a signal processing technique that estimates the state of a dynamic system from a series of noisy measurements. It is used in a wide range of engineering applications from radar to computer vision. This chapter demonstrates the application of a model identification procedure based on extended Kalman filter (EKF) and weighted global iteration (WGI) technique in pavement engineering. In particular, EKF-WGI is used to perform layer moduli back-calculation from falling weight deflectometer (FWD) data and to identify model parameters for Generalized Maxwell Model for hot mix asphalt using frequency sweep test data. In both cases, EKF-WGI is shown to provide consistent results that are independent of the seed values for both linear and nonlinear problems. It is believed that EKF-WGI provides an efficient, consistent and robust tool for optimization that has many potential applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering 82 (Series D), 35–45 (1960)
Welch, G., Bishop, G.: SIGGRAPH 2001 Course 8: An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill, Department of Computer Science. Chapel Hill (2001) NC 27599-3175
Maybeck, P.S.: Stochastic models, estimation, and control. 1 (1979)
Sorenson, H.W.: Least-Squares estimation: from Gauss to Kalman. IEEE Spectrum 7, 63–68 (1970)
Gelb, A.: Applied Optimal Estimation. MIT Press, Cambridge (1974)
Lewis, F.L.: Optimal Estimation with an Introductory to Stochastic Control Theory. John Wiley & Sons, Inc., Chichester (1986)
Jacobs, O.L.R.: Introduction to Control Theory, 2nd edn. Oxford University Press, Oxford (1993)
Brown, R.G., Hwang, P.Y.C.: Introduction to Random Signals and Applied Kalman Filtering: with MATLAB Exercises and Solutions, 3rd edn. Wiley & Sons, Inc., Chichester (1996)
Grewal, M.S., Andrews, P.A.: Kalman Filtering Theory and Practice Using MATLAB, 2nd edn. John Wiley & Sons, Inc, New York (2001)
University of North Carolina: The Kalman Filter, http://www.cs.unc.edu/~welch/kalman/
McGee, L.A., Schmidt, S.F.: Discovery of the Kalman Filter as a Practical Tool for Aerospace and Industry, Technical Memorandum 86847, National Aeronautics and Space Administration (1985)
Asher, R.B., Maybeck, P.S., Mitchell, R.A.K.: Filtering for precision pointing and tracking with application for aircraft to satellite tracking. In: IEEE Conference Decision and Control, Houston, TX (1975)
Halamandaris, H., Ozdes, D.: Kalman Fitering Applied to a Marine Navigation System. In: Proceedings of the 1969 National Marine Navigation Meeting, Second Symposium on Manned Deep Submergence Vehicles, San Diego, CA (1969)
Alexander, H.L., Azarbayejani, A.J., Weigl, H.J.: Kalman-Filter-based machine vision for controlling free-flying unmanned remote vehicles. In: Proceedings of the American Control Conference, Chicago, IL (1992)
Grewal, M.S., Payne, H.J.: Identification of parameters in a freeway traffic model. IEEE Transactions on Systems, Man, and Cybernetics SMC-6, 176–185 (1976)
Potter, J.M.: Kalman Filtering Approach to Market Price Forecasting, Ph.D. Thesis, Iowa State University, Ames (1986)
Prabhu, A.V., Edgar, T.F.: A new state estimation method for high-mix semiconductor manufacturing processes. Journal of Process Control 19(7), 1149–1161 (2009)
Jeon, S., Masayoshi, T., Tetsuaki, K.: A new kinematic Kalman filter (KKF) for end-effector sensing of robotic manipulators. In: The ASME International Mechanical Engineering Congress and Exposition, Seattle, WA (2008)
Hoshiya, M., Saito, E.: Structural Identification by Extended Kalman Filter. Journal of Engineering Mechanics 110(12), 1757–1772 (1984)
Loh, C.-H., Tsaur, Y.-H.: Time Domain Estimation of Structural Parameters. Engineering Structures 10(2), 95–105 (1988)
Bao, Z.-W., Shi, W.-Y.: Parameter identification and state estimation of structure models in shaking table test. In: The 5th International Conference on Structural Safety and Reliability, San Francisco (1989)
Zhang, H.Z., et al.: Parameter identification of inelastic structures under dynamic loads. Earthquake Engineering & Structural Dynamics 31(5), 1113–1130 (2002)
Koh, C.G., See, L.M., Balendra, T.: Determination of storey stiffness of three-dimensional frame buildings. Engineering Structures 17(3), 179–186 (1995)
Anderson, B.D., Moore, J.D.: Optimal Filtering. Prentice Hall, Inc., New Jersey (1979)
Carmichael, D.G.: State Estimation Problem in Experimental Structural Mechanics, National Bureau of Standards. Special Publication (1979)
Yun, C.B., Shinozuka, M.: Identification of Nonlinear Structural Dynamic Systems. Mechanics based design of structures and machines 8(2), 187–203 (1980)
Yun, C.-B.: Damage assessment of bridge structures by system identification, pp. 2179–2186 (1989)
Koh, C.: Estimation of structural parameters in time domain. A substructure approach. Earthquake engineering structural dynamics 20(8), 787–801 (1991)
Seibold, S.: Recursive identification of nonlinear systems by means of the Extended Kalman Filter. In: Proceedings of the International Modal Analysis Conference - IMAC, 1991, vol. 1, pp. 802–809 (1991)
Oreta, A.W.C., Tanabe, T.-a.: Localized identification of structures by Kalman filter. Structural engineering/earthquake engineering 9(4), 217–226 (1993)
Sutoh, A.: Dynamic parameter identification by the EK-WLI-FEM. Doboku Gakkai ronbunshū. 2. Journal of hydraulic, coastal and environmental engineering (477), 97–100 (1993)
Lin, J.-S., Zhang, Y.: Nonlinear structural identification using extended kalman filter. Computers & Structures 52(4), 757–764 (1994)
Oreta, A.W.C., Tanabe, T.-a.: Element Identification of Member Properties of Framed Structures. Journal of Structural Engineering 120(7), 1961–1976 (1994)
Lea, J.: Open Source Pavement Engineering, http://www.openpave.org/
The MathWorks, Matlab, http://www.mathworks.com
Tayebali, A., Deacon, J.A., Monismith, C.L.: Development and evaluation of dynamic flexural beam fatigue test system. Transportation Research Record (1545), 89–97 (1996)
ARA Inc.: Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures, ERES Consultants Division, ARA Inc., National Cooperative Highway Research Program, Transportation Research Board, National Research Council (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Wu, R., Choi, J.W., Harvey, J.T. (2009). Extended Kalman Filter and Its Application in Pavement Engineering. In: Gopalakrishnan, K., Ceylan, H., Attoh-Okine, N.O. (eds) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04586-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-04586-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04585-1
Online ISBN: 978-3-642-04586-8
eBook Packages: EngineeringEngineering (R0)