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Robust extended kalman filter applied to location tracking and trajectory prediction for PCS networks

Pathirana, Pubudu, Savkin, Andrey and Jha, Sanjay 2004, Robust extended kalman filter applied to location tracking and trajectory prediction for PCS networks, in Proceedings of 2004 IEEE Conference on Control Applications : September 2-4, 2004, the Grand Hotel, Taipei, Taiwan, IEEE, Piscataway, N.J., pp. 63-68.

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Title Robust extended kalman filter applied to location tracking and trajectory prediction for PCS networks
Author(s) Pathirana, Pubudu
Savkin, Andrey
Jha, Sanjay
Conference name IEEE International Conference on Control Applications (2004 : Taipei, Taiwan)
Conference location Taipei, Taiwan
Conference dates 2-4 September 2004
Title of proceedings Proceedings of 2004 IEEE Conference on Control Applications : September 2-4, 2004, the Grand Hotel, Taipei, Taiwan
Editor(s) Li-Chen Fu
Publication date 2004
Start page 63
End page 68
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Kalman filters
cellular radio
estimation theory
filtering theory
motion estimation
pattern matching
Summary Provisioning of real-time multimedia sessions over wireless cellular network poses unique challenges due to frequent handoff and rerouting of a connection. For this reason, the wireless networks with cellular architecture require efficient user mobility estimation and prediction. This paper proposes using robust extended Kalman filter (REKF) as a location heading altitude estimator of mobile user for next cell prediction in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm reduces the system complexity (compared to existing approach using pattern matching and Kalman filter) as it requires only two base station measurements or only the measurement from the closest base station. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model and more effective in comparison with the standard Kalman filter.
ISBN 0780386337
0780386345
Language eng
Field of Research 100503 Computer Communications Networks
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2004, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005385

Document type: Conference Paper
Collection: School of Engineering and Technology
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