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Location estimation and trajectory prediction for cellular networks with mobile base stations

Pathirana, Pubudu, Savkin, Andrey and Jha, Sanjay 2004, Location estimation and trajectory prediction for cellular networks with mobile base stations, IEEE transactions on vehicular technology, vol. 53, no. 6, pp. 1903-1913.

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Title Location estimation and trajectory prediction for cellular networks with mobile base stations
Author(s) Pathirana, Pubudu
Savkin, Andrey
Jha, Sanjay
Journal name IEEE transactions on vehicular technology
Volume number 53
Issue number 6
Start page 1903
End page 1913
Publisher Institute of Electrical and Electronics Engineers
Place of publication New York, N.Y.
Publication date 2004-11
ISSN 0018-9545
Keyword(s) ad hoc networks
CarNet
location tracking
mobility modeling
robust extended Kalman filter
Notes This paper provides mobility estimation and prediction for a variant of the GSM network that resembles an ad hoc wireless mobile network in which base stations and users are both mobile. We propose using a Robust Extended Kalman Filter (REKF) to derive an estimate of the mobile user's next mobile base station from the user's location, heading, and altitude, to improve connection reliability and bandwidth efficiency of the underlying system. Our analysis demonstrates that our algorithm can successfully track the mobile users with less system complexity, as it requires measurements from only one or two closest mobile base stations. Further, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.
Language eng
Field of Research 100503 Computer Communications Networks
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30002586

Document type: Journal Article
Collection: School of Engineering and Technology
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Created: Mon, 07 Jul 2008, 08:28:53 EST