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Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters. / Mihaylova, L.; Bull, D.; Angelova, D. et al.
2005 8th International Conference on Information Fusion Proceedings. 2005.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Mihaylova, L, Bull, D, Angelova, D & Canagarajah, N 2005, Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters. in 2005 8th International Conference on Information Fusion Proceedings. Eighth International Conf. on Information Fusion, Philadelphia, USA, 25/07/05. https://doi.org/10.1109/ICIF.2005.1591843

APA

Mihaylova, L., Bull, D., Angelova, D., & Canagarajah, N. (2005). Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters. In 2005 8th International Conference on Information Fusion Proceedings https://doi.org/10.1109/ICIF.2005.1591843

Vancouver

Mihaylova L, Bull D, Angelova D, Canagarajah N. Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters. In 2005 8th International Conference on Information Fusion Proceedings. 2005 doi: 10.1109/ICIF.2005.1591843

Author

Mihaylova, L. ; Bull, D. ; Angelova, D. et al. / Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters. 2005 8th International Conference on Information Fusion Proceedings. 2005.

Bibtex

@inproceedings{703a590705444dd7806b7f8a5069db2b,
title = "Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters",
abstract = "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.",
keywords = "mobility tracking, Monte Carlo methods, wireless networks, hybrid systems, Rao-Blackwellisation, Singer model, DCS-publications-id, inproc-427, DCS-publications-credits, dsp-fa, DCS-publications-personnel-id, 121",
author = "L. Mihaylova and D. Bull and D. Angelova and N. Canagarajah",
note = "doi:10.1109/ICIF.2005.1591843 {"}{\textcopyright}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.{"}; Eighth International Conf. on Information Fusion ; Conference date: 25-07-2005 Through 28-07-2005",
year = "2005",
month = jul,
day = "25",
doi = "10.1109/ICIF.2005.1591843",
language = "English",
isbn = "0-7803-9286-8",
booktitle = "2005 8th International Conference on Information Fusion Proceedings",

}

RIS

TY - GEN

T1 - Mobility Tracking in Cellular Networks with Sequential Monte Carlo Filters

AU - Mihaylova, L.

AU - Bull, D.

AU - Angelova, D.

AU - Canagarajah, N.

N1 - 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."

PY - 2005/7/25

Y1 - 2005/7/25

N2 - 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.

AB - 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.

KW - mobility tracking

KW - Monte Carlo methods

KW - wireless networks

KW - hybrid systems

KW - Rao-Blackwellisation

KW - Singer model

KW - DCS-publications-id

KW - inproc-427

KW - DCS-publications-credits

KW - dsp-fa

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1109/ICIF.2005.1591843

DO - 10.1109/ICIF.2005.1591843

M3 - Conference contribution/Paper

SN - 0-7803-9286-8

BT - 2005 8th International Conference on Information Fusion Proceedings

T2 - Eighth International Conf. on Information Fusion

Y2 - 25 July 2005 through 28 July 2005

ER -