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Robust Kalman Filtering With Probabilistic Uncertainty in System Parameters


Abstract:

In this letter, we propose a robust Kalman filtering framework for systems with probabilistic uncertainty in system parameters. We consider two cases, namely discrete tim...Show More

Abstract:

In this letter, we propose a robust Kalman filtering framework for systems with probabilistic uncertainty in system parameters. We consider two cases, namely discrete time systems, and continuous time systems with discrete measurements. The uncertainty, characterized by mean and variance of the states, is propagated using conditional expectations and polynomial chaos expansion framework. The results obtained using the proposed filter are compared with existing robust filters in the literature. The proposed filter demonstrates better performance in terms of estimation error and rate of convergence.
Published in: IEEE Control Systems Letters ( Volume: 5, Issue: 1, January 2021)
Page(s): 295 - 300
Date of Publication: 10 June 2020
Electronic ISSN: 2475-1456

Funding Agency:


References

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