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Maximum correntropy unscented filter.

Authors :
Liu, Xi
Chen, Badong
Xu, Bin
Wu, Zongze
Honeine, Paul
Source :
International Journal of Systems Science. Jun2017, Vol. 48 Issue 8, p1607-1615. 9p.
Publication Year :
2017

Abstract

The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilising a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimator leads to the well-known unscented Kalman filter (UKF). Although the UKF works very well in Gaussian noises, its performance may deteriorate significantly when the noises are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises. To improve the robustness of the UKF against impulsive noises, a new filter for non-linear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF). In MCUF, the UT is applied to obtain the prior estimates of the state and covariance matrix, and a robust statistical linearisation regression based on the maximum correntropy criterion is then used to obtain the posterior estimates of the state and covariance matrix. The satisfying performance of the new algorithm is confirmed by two illustrative examples. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00207721
Volume :
48
Issue :
8
Database :
Academic Search Index
Journal :
International Journal of Systems Science
Publication Type :
Academic Journal
Accession number :
121746131
Full Text :
https://doi.org/10.1080/00207721.2016.1277407