Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise
(2016) 19th International Conference on Information Fusion (FUSION)- 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 non-additively. 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... (More)
- 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 non-additively. 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/016f1c4d-2556-4dcd-b78d-4b19b10f7d14
- author
- Tronarp, Filip LU ; Hostettler, Roland and Särkkä, Simo
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 19th International Conference on Information Fusion (FUSION)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 19th International Conference on Information Fusion (FUSION)
- conference location
- Heidelberg, Germany
- conference dates
- 2016-07-05 - 2016-07-08
- ISBN
- 978-0-9964-5274-8
- language
- English
- LU publication?
- no
- id
- 016f1c4d-2556-4dcd-b78d-4b19b10f7d14
- date added to LUP
- 2023-08-20 22:30:29
- date last changed
- 2023-10-09 18:04:52
@inproceedings{016f1c4d-2556-4dcd-b78d-4b19b10f7d14, 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 non-additively. 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.}}, author = {{Tronarp, Filip and Hostettler, Roland and Särkkä, Simo}}, booktitle = {{19th International Conference on Information Fusion (FUSION)}}, isbn = {{978-0-9964-5274-8}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise}}, year = {{2016}}, }