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State-Space Model and Kalman Filter Gain Identification by a Superspace Method

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Modeling, Simulation and Optimization of Complex Processes - HPSC 2012

Abstract

This paper describes a superspace method to identify a state-space model and an associated Kalman filter gain from input-output data. Superstate vectors are simply vectors containing input-output measurements, and used directly for the identification. The superstate space is unusual in that the state portion of the Kalman filter becomes completely independent of both the system dynamics and the input and output noise statistics. The system dynamics is entirely carried by the measurement portion of the superstate Kalman filter model. When model reduction is applied, the system dynamics returns to the state portion of the state-space model.

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Acknowledgements

This research is supported by a STTR Phase II contract from the US Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL) to Dartmouth College and Sound Innovations, Inc.

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Authors and Affiliations

  1. Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA

    Ping Lin & Minh Q. Phan

  2. Cold Regions Research and Engineering Laboratory (CRREL), Hanover, NH, 03755, USA

    Stephen A. Ketcham

Authors
  1. Ping Lin
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  2. Minh Q. Phan
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  3. Stephen A. Ketcham
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Corresponding author

Correspondence to Minh Q. Phan .

Editor information

Editors and Affiliations

  1. Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany

    Hans Georg Bock

  2. Vietnam Academy of Science and Technology(VAST), Hanoi, Vietnam

    Xuan Phu Hoang

  3. Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany

    Rolf Rannacher

  4. Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany

    Johannes P. Schlöder

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© 2014 Springer International Publishing Switzerland

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Lin, P., Phan, M.Q., Ketcham, S.A. (2014). State-Space Model and Kalman Filter Gain Identification by a Superspace Method. In: Bock, H., Hoang, X., Rannacher, R., Schlöder, J. (eds) Modeling, Simulation and Optimization of Complex Processes - HPSC 2012. Springer, Cham. https://doi.org/10.1007/978-3-319-09063-4_10

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