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Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters

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This paper considers mobility tracking in wireless communication networks based on received signal strength indicator measurements. Mobility tracking involves on-line estimation of the position and speed of a mobile unit. Mobility tracking is formulated as an estimation problem of a hybrid system consisting of a base state vector and a modal state vector. The command is modelled as a first-order Markov process which can take values from a finite set of acceleration levels. In order to cover the wide range of acceleration changes, a set of acceleration values is predetermined. Sequential Monte Carlo algorithms – a particle filter (PF) and a Rao-Blackwellised particle filter (RBPF) are proposed and their performance evaluated over a synthetic data example.

Bibliographic note

doi:10.1109/ICIF.2005.1591843 "©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."