By Topic

Comparative Statistical Analysis of New Adaptive Filtering Techniques for Precise Indoor Local Positioning

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

2 Author(s)
Qasem, H. ; Freiburg Univ., Freiburg ; Reindl, L.

This paper compares two different recursive tracking techniques for precisely localizing a mobile vehicle in an indoor harsh industrial environment. An extended Kalman filter (EKF) and unscented Kalman filter (UKF), the corresponding algorithms and mathematical models are presented and analysed. Experimental range measurements generated from local positioning radar system are used to test the performance of these algorithms with respect to position and velocity root mean square errors. True and estimated trajectories of the mobile vehicle with associated means and error covariances are illustrated with the number of samples required in each case. Results obtained show that UKF outer performs EKF with respect to positioning accuracy and root mean square error. Both filters show comparable computational complexity with more robustness obtained by applying UKF for non linear estimation since there are no linearization errors as in the case of EKF.

Published in:

Mobile and Wireless Communications Summit, 2007. 16th IST

Date of Conference:

1-5 July 2007