State estimation under non-Gaussian Levy noise: A modified Kalman filtering method
Dynamical Systems
2013-03-12 v1 Information Theory
Machine Learning
math.IT
Probability
Machine Learning
Abstract
The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian L\'evy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian L\'evy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian L\'evy noise is devised. It works effectively with reasonable computational cost. Simulation results are presented to illustrate this non-Gaussian filtering method.
Cite
@article{arxiv.1303.2395,
title = {State estimation under non-Gaussian Levy noise: A modified Kalman filtering method},
author = {Xu Sun and Jinqiao Duan and Xiaofan Li and Xiangjun Wang},
journal= {arXiv preprint arXiv:1303.2395},
year = {2013}
}