Subspace-based noise covariance estimation for Kalman filter in virtual sensing applications

Author:

Greś SzymonORCID,Döhler MichaelORCID,Dertimanis Vasilis K.ORCID,Chatzi Eleni N.ORCID

Funder

Danmarks Frie Forskningsfond

DFF

Publisher

Elsevier BV

Reference38 articles.

1. A physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing;McClellan;Phil. Trans. R. Soc. A,2022

2. Virtual sensing for SHM: a comparison between Kalman filters and Gaussian processes;Papatheou,2022

3. Subspace Identification for Linear Systems;van Overschee,1996

4. System identification methods for (operational) modal analysis: Review and comparison;Reynders;Arch. Comput. Methods Eng.,2012

5. Experimental validation of the Kalman-type filters for online and real-time state and input estimation;Azam;J. Vib. Control,2017

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