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Numerically robust square root implementations of statistical linear regression filters and smoothers

Tronarp, Filip LU (2024) 32nd European Signal Processing Conference, EUSIPCO 2024 p.2597-2601
Abstract

In this article, square-root formulations of the statistical linear regression filter and smoother are developed. Crucially, the method uses QR decompositions rather than Cholesky downdates. This makes the method inherently more numerically robust than the downdate based methods, which may fail in the face of rounding errors. This increased robustness is demonstrated in an ill-conditioned problem, where it is compared against a reference implementation in both double and single precision arithmetic. The new implementation is found to be more robust, when implemented in lower precision arithmetic as compared to the alternative.

Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Gaussian filtering, Gaussian smoothing, Statistical Linear Regression
host publication
32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
pages
5 pages
publisher
European Signal Processing Conference, EUSIPCO
conference name
32nd European Signal Processing Conference, EUSIPCO 2024
conference location
Lyon, France
conference dates
2024-08-26 - 2024-08-30
external identifiers
  • scopus:85208416380
ISBN
9789464593617
DOI
10.23919/eusipco63174.2024.10715204
language
English
LU publication?
yes
id
0b6a2235-0524-4854-bccc-a1036e0450a7
date added to LUP
2025-02-18 09:46:37
date last changed
2025-04-04 15:07:31
@inproceedings{0b6a2235-0524-4854-bccc-a1036e0450a7,
  abstract     = {{<p>In this article, square-root formulations of the statistical linear regression filter and smoother are developed. Crucially, the method uses QR decompositions rather than Cholesky downdates. This makes the method inherently more numerically robust than the downdate based methods, which may fail in the face of rounding errors. This increased robustness is demonstrated in an ill-conditioned problem, where it is compared against a reference implementation in both double and single precision arithmetic. The new implementation is found to be more robust, when implemented in lower precision arithmetic as compared to the alternative.</p>}},
  author       = {{Tronarp, Filip}},
  booktitle    = {{32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings}},
  isbn         = {{9789464593617}},
  keywords     = {{Gaussian filtering; Gaussian smoothing; Statistical Linear Regression}},
  language     = {{eng}},
  pages        = {{2597--2601}},
  publisher    = {{European Signal Processing Conference, EUSIPCO}},
  title        = {{Numerically robust square root implementations of statistical linear regression filters and smoothers}},
  url          = {{http://dx.doi.org/10.23919/eusipco63174.2024.10715204}},
  doi          = {{10.23919/eusipco63174.2024.10715204}},
  year         = {{2024}},
}