By Topic

Kalman Filtering with Uncertain Process and Measurement Noise Covariances with Application to State Estimation in Sensor Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ling Shi ; Control & Dynamical Syst., California Inst. of Technol., Pasadena, CA ; Johansson, K.H. ; Murray, R.M.

Distributed state estimation under uncertain process and measurement noise covariances is considered. An algorithm based on sensor fusion using Kalman filtering is investigated. It is shown that if the covariances are decomposed into a known nominal covariance plus an uncertainty term, then the uncertainty of the actual estimation error covariance for the Kalman filter grows linearly with the size of the uncertainty term. This result is extended to the sensor fusion scheme to give an upper bound on the actual error covariance for the fused state estimate. Examples are provided to illustrate how the theory can be applied in practice.

Published in:

Control Applications, 2007. CCA 2007. IEEE International Conference on

Date of Conference:

1-3 Oct. 2007