MSE-optimal measurement dimension reduction in gaussian filtering
(2020) 4th IEEE Conference on Control Technology and Applications, CCTA 2020 In CCTA 2020 - 4th IEEE Conference on Control Technology and Applications p.126-133- Abstract
We present a framework for measurement dimension reduction in Gaussian filtering, defined in terms of a linear operator acting on the measurement vector. This operator is optimized to minimize the Cramér-Rao bound of the estimate's mean squared error (MSE), yielding a measurement subspace from which elements minimally worsen the filter MSE performance, as compared to filtering with the original measurements. This is demonstrated with Kalman filtering in a linear Gaussian setting and various non-linear Gaussian filters with an on-line adaption of the operator. The proposed method improves computational time in exchange for a quantifiable and sometimes negligibly worsened MSE of the estimate.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/82086b57-b7ee-431f-8f6f-5926e546aa70
- author
- Greiff, Marcus LU ; Robertsson, Anders LU and Berntorp, Karl LU
- organization
- publishing date
- 2020-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- CCTA 2020 - 4th IEEE Conference on Control Technology and Applications
- series title
- CCTA 2020 - 4th IEEE Conference on Control Technology and Applications
- article number
- 9206162
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 4th IEEE Conference on Control Technology and Applications, CCTA 2020
- conference location
- Virtual, Montreal, Canada
- conference dates
- 2020-08-24 - 2020-08-26
- external identifiers
-
- scopus:85094159980
- ISBN
- 9781728171401
- DOI
- 10.1109/CCTA41146.2020.9206162
- language
- English
- LU publication?
- yes
- id
- 82086b57-b7ee-431f-8f6f-5926e546aa70
- date added to LUP
- 2020-11-10 12:12:40
- date last changed
- 2022-05-12 07:41:05
@inproceedings{82086b57-b7ee-431f-8f6f-5926e546aa70, abstract = {{<p>We present a framework for measurement dimension reduction in Gaussian filtering, defined in terms of a linear operator acting on the measurement vector. This operator is optimized to minimize the Cramér-Rao bound of the estimate's mean squared error (MSE), yielding a measurement subspace from which elements minimally worsen the filter MSE performance, as compared to filtering with the original measurements. This is demonstrated with Kalman filtering in a linear Gaussian setting and various non-linear Gaussian filters with an on-line adaption of the operator. The proposed method improves computational time in exchange for a quantifiable and sometimes negligibly worsened MSE of the estimate.</p>}}, author = {{Greiff, Marcus and Robertsson, Anders and Berntorp, Karl}}, booktitle = {{CCTA 2020 - 4th IEEE Conference on Control Technology and Applications}}, isbn = {{9781728171401}}, language = {{eng}}, pages = {{126--133}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{CCTA 2020 - 4th IEEE Conference on Control Technology and Applications}}, title = {{MSE-optimal measurement dimension reduction in gaussian filtering}}, url = {{http://dx.doi.org/10.1109/CCTA41146.2020.9206162}}, doi = {{10.1109/CCTA41146.2020.9206162}}, year = {{2020}}, }