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OKID as a Unified Approach to System Identification

Vicario, Francesco; Phan, Minh Q.; Betti, Raimondo; Longman, Richard W.

This paper presents a unified approach for the identification of linear state-space models from input-output measurements in the presence of noise. It is based on the established Observer/Kalman filter IDentification (OKID) method of which it proposes a new formulation capable of transforming a stochastic identification problem into a (simpler) deterministic problem, where the Kalman filter corresponding to the unknown system and the unknown noise covariances is identified. The system matrices are then recovered from the identified Kalman filter. The Kalman filter can be identified with any deterministic identification method for linear state-space models, giving rise to numerous new algorithms and establishing the Kalman filter as the unifying bridge from stochastic to deterministic problems in system identification.


Also Published In

Spaceflight mechanics 2014 : proceedings of the 24th AAS/AIAA Space Flight Mechanics Meeting

More About This Work

Academic Units
Mechanical Engineering
Published Here
January 5, 2016


This work was presented at the 24th AAS/AIAA Space Flight Mechanics Meeting, Santa Fe, NM, January 2014. The paper (identified as paper AAS 14-450) was then published as part of the conference proceedings in the Advances in the Astronautical Sciences, Vol. 152, 2014, pp. 3443-3460 (published by Univelt).