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

Ping Lin (), Minh Q. Phan () and Stephen A. Ketcham ()
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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)

A chapter in Modeling, Simulation and Optimization of Complex Processes - HPSC 2012, 2014, pp 121-132 from Springer

Abstract: 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.

Keywords: Kalman Filter; Subspace Method; State Portion; System Identification Problem; Measurement Noise Covariance (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-09063-4_10

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DOI: 10.1007/978-3-319-09063-4_10

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