Advanced

Distributed Kalman Filtering Using Weighted Averaging

Alriksson, Peter LU and Rantzer, Anders LU (2006) 17th International Symposium on Mathematical Theory of Networks and Systems, 2006
Abstract
This paper addresses the problem of distributed Kalman filtering, with

focus on limiting the required communication bandwidth.

By distributed we refer to a scenario when all nodes in the network desire an

estimate of the full state of the observed system and there is no

centralized computation center. Communication only takes place

between neighbors and only a fixed number of times each sample. To

reduce bandwidth requirements of individual nodes, estimates

instead of measurements are communicated. A new estimate is

then formed as a weighted average of the neighbouring estimates. The

weights are optimized to yield a small estimation error covariance... (More)
This paper addresses the problem of distributed Kalman filtering, with

focus on limiting the required communication bandwidth.

By distributed we refer to a scenario when all nodes in the network desire an

estimate of the full state of the observed system and there is no

centralized computation center. Communication only takes place

between neighbors and only a fixed number of times each sample. To

reduce bandwidth requirements of individual nodes, estimates

instead of measurements are communicated. A new estimate is

then formed as a weighted average of the neighbouring estimates. The

weights are optimized to yield a small estimation error covariance in

stationarity. The minimization can be done off line thus allowing

only estimates to be communicated. The advantage of communicating

estimates instead of measurements becomes more evident when the number

of nodes exceeds the size of the state vector to be estimated. The

algorithm is applied to one

simple second order system and temperature sensing network. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
conference name
17th International Symposium on Mathematical Theory of Networks and Systems, 2006
language
English
LU publication?
yes
id
02021ade-8ae7-4cfe-91be-0eb63bb0d4c9 (old id 930205)
date added to LUP
2008-01-15 09:53:42
date last changed
2016-06-21 16:00:59
@misc{02021ade-8ae7-4cfe-91be-0eb63bb0d4c9,
  abstract     = {This paper addresses the problem of distributed Kalman filtering, with<br/><br>
focus on limiting the required communication bandwidth.<br/><br>
By distributed we refer to a scenario when all nodes in the network desire an<br/><br>
estimate of the full state of the observed system and there is no<br/><br>
centralized computation center. Communication only takes place<br/><br>
between neighbors and only a fixed number of times each sample. To<br/><br>
reduce bandwidth requirements of individual nodes, estimates<br/><br>
instead of measurements are communicated. A new estimate is<br/><br>
then formed as a weighted average of the neighbouring estimates. The<br/><br>
weights are optimized to yield a small estimation error covariance in<br/><br>
stationarity. The minimization can be done off line thus allowing<br/><br>
only estimates to be communicated. The advantage of communicating<br/><br>
estimates instead of measurements becomes more evident when the number<br/><br>
of nodes exceeds the size of the state vector to be estimated. The<br/><br>
algorithm is applied to one<br/><br>
simple second order system and temperature sensing network.},
  author       = {Alriksson, Peter and Rantzer, Anders},
  language     = {eng},
  title        = {Distributed Kalman Filtering Using Weighted Averaging},
  year         = {2006},
}