Scalable Distributed Kalman Filtering for Mass-Spring Systems
Henningsson, Toivo LU and Rantzer, Anders LU
(2007)
46th IEEE Conference on Decision and Control, 2007
- Abstract
- This paper considers Kalman Filtering for massspring systems. The aim is a scalable distributed implementation where nodes communicate in a sparse pattern and the state estimate for each node is available locally and usable for control. The focus is on translation invariant systems, to make use of the powerful results available based on Fourier Transform methods. In this case it is known that Kalman Filters will have a coupling that asymptotically falls off exponentially with distance. Examples are shown where the Kalman Filter gains can be truncated very narrowly with small performance loss even though the coupling falls off slowly. A step towards spatially varying systems is taken in analyzing a system with periodically placed sensors,... (More)
- This paper considers Kalman Filtering for massspring systems. The aim is a scalable distributed implementation where nodes communicate in a sparse pattern and the state estimate for each node is available locally and usable for control. The focus is on translation invariant systems, to make use of the powerful results available based on Fourier Transform methods. In this case it is known that Kalman Filters will have a coupling that asymptotically falls off exponentially with distance. Examples are shown where the Kalman Filter gains can be truncated very narrowly with small performance loss even though the coupling falls off slowly. A step towards spatially varying systems is taken in analyzing a system with periodically placed sensors, and it is shown that the original design is insensitive to this spatial variation. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1003053
- author
- Henningsson, Toivo
LU
and Rantzer, Anders
LU
- organization
- publishing date
- 2007
- type
- Contribution to conference
- publication status
- published
- subject
- keywords
- Kalman Filtering, distributed estimation, flexible structures
- conference name
- 46th IEEE Conference on Decision and Control, 2007
- conference location
- New Orleans, LA, United States
- conference dates
- 2007-12-12 - 2007-12-14
- external identifiers
-
- scopus:62749131191
- language
- English
- LU publication?
- yes
- id
- 181b328e-0e77-4102-ad80-0bd1cb222404 (old id 1003053)
- date added to LUP
- 2016-04-04 13:26:37
- date last changed
- 2023-09-06 16:44:55
@misc{181b328e-0e77-4102-ad80-0bd1cb222404,
abstract = {{This paper considers Kalman Filtering for massspring systems. The aim is a scalable distributed implementation where nodes communicate in a sparse pattern and the state estimate for each node is available locally and usable for control. The focus is on translation invariant systems, to make use of the powerful results available based on Fourier Transform methods. In this case it is known that Kalman Filters will have a coupling that asymptotically falls off exponentially with distance. Examples are shown where the Kalman Filter gains can be truncated very narrowly with small performance loss even though the coupling falls off slowly. A step towards spatially varying systems is taken in analyzing a system with periodically placed sensors, and it is shown that the original design is insensitive to this spatial variation.}},
author = {{Henningsson, Toivo and Rantzer, Anders}},
keywords = {{Kalman Filtering; distributed estimation; flexible structures}},
language = {{eng}},
title = {{Scalable Distributed Kalman Filtering for Mass-Spring Systems}},
url = {{https://lup.lub.lu.se/search/files/6121228/8862995.pdf}},
year = {{2007}},
}