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

In: Modeling, Simulation and Optimization of Complex Processes - HPSC 2012

Author

Listed:
  • Ping Lin

    (Dartmouth College, Thayer School of Engineering)

  • Minh Q. Phan

    (Dartmouth College, Thayer School of Engineering)

  • Stephen A. Ketcham

    (Cold Regions Research and Engineering Laboratory (CRREL))

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.

Suggested Citation

  • Ping Lin & Minh Q. Phan & Stephen A. Ketcham, 2014. "State-Space Model and Kalman Filter Gain Identification by a Superspace Method," Springer Books, in: Hans Georg Bock & Xuan Phu Hoang & Rolf Rannacher & Johannes P. Schlöder (ed.), Modeling, Simulation and Optimization of Complex Processes - HPSC 2012, edition 127, pages 121-132, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-09063-4_10
    DOI: 10.1007/978-3-319-09063-4_10
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