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Continuous-discrete filtering and smoothing on submanifolds of euclidean space

Tronarp, Filip LU and Särkkä, Simo (2022) 25th International Conference on Information Fusion, FUSION 2022
Abstract
In this paper the issue of filtering and smoothing in continuous discrete time is studied when the state variable evolves in some submanifold of Euclidean space, which may not have the usual Lebesgue measure. Formal expressions for prediction and smoothing problems are reviewed, which agree with the classical results except that the formal adjoint of the generator is different in general. These results are used to generalise the projection approach to filtering and smoothing to the case when the state variable evolves in some submanifold that lacks a Lebesgue measure. The approach is used to develop projection filters and smoothers based on the von Mises–Fisher distribution, which are shown to be outperform Gaussian estimators both in... (More)
In this paper the issue of filtering and smoothing in continuous discrete time is studied when the state variable evolves in some submanifold of Euclidean space, which may not have the usual Lebesgue measure. Formal expressions for prediction and smoothing problems are reviewed, which agree with the classical results except that the formal adjoint of the generator is different in general. These results are used to generalise the projection approach to filtering and smoothing to the case when the state variable evolves in some submanifold that lacks a Lebesgue measure. The approach is used to develop projection filters and smoothers based on the von Mises–Fisher distribution, which are shown to be outperform Gaussian estimators both in terms of estimation accuracy and computational speed in simulation experiments involving the tracking of a gravity vector. (Less)
Please use this url to cite or link to this publication:
author
and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
25th International Conference on Information Fusion (FUSION)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
25th International Conference on Information Fusion, FUSION 2022
conference location
Linkoping, Sweden
conference dates
2022-07-04 - 2022-07-07
external identifiers
  • scopus:85136537070
ISBN
978-1-6654-8941-6
978-1-7377497-2-1
DOI
10.23919/FUSION49751.2022.9841226
language
English
LU publication?
no
id
3e5e9153-32a1-4fc1-9671-e77ce109b583
date added to LUP
2023-08-21 02:12:39
date last changed
2024-04-05 22:42:42
@inproceedings{3e5e9153-32a1-4fc1-9671-e77ce109b583,
  abstract     = {{In this paper the issue of filtering and smoothing in continuous discrete time is studied when the state variable evolves in some submanifold of Euclidean space, which may not have the usual Lebesgue measure. Formal expressions for prediction and smoothing problems are reviewed, which agree with the classical results except that the formal adjoint of the generator is different in general. These results are used to generalise the projection approach to filtering and smoothing to the case when the state variable evolves in some submanifold that lacks a Lebesgue measure. The approach is used to develop projection filters and smoothers based on the von Mises–Fisher distribution, which are shown to be outperform Gaussian estimators both in terms of estimation accuracy and computational speed in simulation experiments involving the tracking of a gravity vector.}},
  author       = {{Tronarp, Filip and Särkkä, Simo}},
  booktitle    = {{25th International Conference on Information Fusion (FUSION)}},
  isbn         = {{978-1-6654-8941-6}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Continuous-discrete filtering and smoothing on submanifolds of euclidean space}},
  url          = {{http://dx.doi.org/10.23919/FUSION49751.2022.9841226}},
  doi          = {{10.23919/FUSION49751.2022.9841226}},
  year         = {{2022}},
}