The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian Lévy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian Lévy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian Lévy noise is devised. It works effectively with reasonable computational cost. Simulation results are presented to illustrate this non-Gaussian filtering method.
@article{bwmeta1.element.bwnjournal-article-doi-10_4064-bc105-0-14,
author = {Xu Sun and Jinqiao Duan and Xiaofan Li and Xiangjun Wang},
title = {State estimation under non-Gaussian L\'evy noise: A modified Kalman filtering method},
journal = {Banach Center Publications},
volume = {104},
year = {2015},
pages = {239-246},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-doi-10_4064-bc105-0-14}
}
Xu Sun; Jinqiao Duan; Xiaofan Li; Xiangjun Wang. State estimation under non-Gaussian Lévy noise: A modified Kalman filtering method. Banach Center Publications, Tome 104 (2015) pp. 239-246. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_4064-bc105-0-14/