Sigma-Point Filtering for Nonlinear Systems with Non-Additive Heavy-Tailed Noise

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2016-07-05
Major/Subject
Mcode
Degree programme
Language
en
Pages
1859 - 1866
Series
Proceedings of the 19th International Conference on Information Fusion, FUSION 2016
Abstract
This paper is concerned with sigma-point methods for filtering in nonlinear systems, where the process and measurement noise are heavy tailed and enter the system nonadditively. The problem is approached within the framework of assumed density filtering and the necessary statistics are approximated using sigma-point methods developed for Student’s t-distribution. This leads to UKF/CKF-type of filters for Student’s t-distribution. Four different sigma-point methods are considered that compute exact expectations of polynomials for orders up to 3, 5, 7, and 9, respectively. The resulting algorithms are evaluated in a simulation example and real data from a pedestrian dead-reckoning experiment. In the simulation experiment the nonlinear Student’s t filters are found to be faster in suppressing large errors in the state estimates in comparison to the UKF when filtering in nonlinear Gaussian systems with outliers in process and measurement noise. In the pedestrian dead-reckoning experiment the sigma-point Student’s t filter was found to yield better loop closure and path length estimates as well as significantly improved robustness towards extreme accelerometer measurement spikes.
Description
Keywords
Other note
Citation
Tronarp , F , Hostettler , R & Särkkä , S 2016 , Sigma-Point Filtering for Nonlinear Systems with Non-Additive Heavy-Tailed Noise . in Proceedings of the 19th International Conference on Information Fusion, FUSION 2016 . , 7528109 , IEEE , pp. 1859 - 1866 , International Conference on Information Fusion , Heidelberg , Germany , 05/07/2016 . < http://ieeexplore.ieee.org/document/7528109/ >