An Adaptive Scheme Based on Distributed Kalman Filter for Sensor Network

DOI

10.2991/iccasm.2012.353How to use a DOI?

Keywords

Adaptive distributed Kalman filter, sensor network, consensus filters

Abstract

Aiming at reducing the kinematic model error and uncertain measurement noise in sensor network, an adaptive scheme for distributed Kalman filter (DKF) is proposed in this paper. An adaptive factor is firstly applied to the covariance matrix of the predicted state vector to make the covariance estimation agree with its theoretical one, thus eliminating the effect of kinematic model error. Based on the innovation covariance of measurement noise and an adjustable fading factor, the other adaptive strategy is further developed for the updating of the covariance matrix of the measurement noise, which contributes to reduce the effect of the uncertain measurement noise. As demonstrated in simulation results, the adaptive distributed Kalman filter (ADKF) for sensor network performs better than the plain DKF in the case of suffering the kinematic model error and uncertain measurement noise.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kezhi Zhang
PY  - 2012/08
DA  - 2012/08
TI  - An Adaptive Scheme Based on Distributed Kalman Filter for Sensor Network
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 1381
EP  - 1384
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.353
DO  - 10.2991/iccasm.2012.353
ID  - Zhang2012/08
ER  -