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Exploiting linear substructure in linear regression Kalman filters

Greiff, Marcus LU ; Robertsson, Anders LU and Berntorp, Karl LU (2021) 59th IEEE Annual Conference on Decision and Control p.2942-2948
Abstract
We exploit knowledge of linear substructure in the linear-regression Kalman filters (LRKFs) to simplify the problem of moment matching. The theoretical results yield quantifiable and significant computational speedups at no cost of estimation accuracy, assuming partially linear estimation models. The results apply to any symmetrical LRKF, and reductions in computational complexity are stated as a function of the cubature rule, the number of linear and nonlinear states in the estimation model respectively. The implications for the filtering problem are illustrated by several numerical examples.
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
59th Conference on Decision and Control (CDC 2020)
pages
2942 - 2948
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
59th IEEE Annual Conference on Decision and Control
conference location
Jeju Island (online), Korea, Republic of
conference dates
2020-12-14 - 2020-12-18
external identifiers
  • scopus:85099885103
ISBN
978-1-7281-7447-1
DOI
10.1109/CDC42340.2020.9304191
project
Semantic Mapping and Visual Navigation for Smart Robots
language
English
LU publication?
yes
id
16e4be39-7731-436e-9196-79b3eccbc05e
date added to LUP
2021-01-10 23:39:20
date last changed
2022-05-12 17:16:24
@inproceedings{16e4be39-7731-436e-9196-79b3eccbc05e,
  abstract     = {{We exploit knowledge of linear substructure in the linear-regression Kalman filters (LRKFs) to simplify the problem of moment matching. The theoretical results yield quantifiable and significant computational speedups at no cost of estimation accuracy, assuming partially linear estimation models. The results apply to any symmetrical LRKF, and reductions in computational complexity are stated as a function of the cubature rule, the number of linear and nonlinear states in the estimation model respectively. The implications for the filtering problem are illustrated by several numerical examples.}},
  author       = {{Greiff, Marcus and Robertsson, Anders and Berntorp, Karl}},
  booktitle    = {{59th Conference on Decision and Control (CDC 2020)}},
  isbn         = {{978-1-7281-7447-1}},
  language     = {{eng}},
  pages        = {{2942--2948}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Exploiting linear substructure in linear regression Kalman filters}},
  url          = {{http://dx.doi.org/10.1109/CDC42340.2020.9304191}},
  doi          = {{10.1109/CDC42340.2020.9304191}},
  year         = {{2021}},
}