Kalman Filtering with Probabilistic Uncertainty in System Parameters
Systems and Control
2020-07-09 v3 Systems and Control
Abstract
In this paper, 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 root mean squared error and rate of convergence.
Cite
@article{arxiv.2003.10926,
title = {Kalman Filtering with Probabilistic Uncertainty in System Parameters},
author = {Sunsoo Kim and Vedang M. Deshpande and Raktim Bhattacharya},
journal= {arXiv preprint arXiv:2003.10926},
year = {2020}
}