Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Mobility Tracking in Cellular Networks Using Particle Filtering.
AU - Mihaylova, L
AU - Angelova, D
AU - Honary, S
AU - Bull, D
AU - Canagarajah, N
AU - Ristic, B
N1 - "©2007 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 - 2007/10
Y1 - 2007/10
N2 - Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of Mobile Station’s (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an Extended Kalman Filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cram´er Rao lower bound (PCRLB) is calculated and it is compared with the filters’ rootmean-square error performance.
AB - Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of Mobile Station’s (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an Extended Kalman Filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cram´er Rao lower bound (PCRLB) is calculated and it is compared with the filters’ rootmean-square error performance.
KW - Mobility tracking
KW - wireless networks
KW - hybrid systems
KW - sequential Monte Carlo methods
KW - Rao-Blackwellisation
KW - DCS-publications-id
KW - art-865
KW - DCS-publications-credits
KW - coding
KW - DCS-publications-personnel-id
KW - 121
U2 - 10.1109/TWC.2007.05912
DO - 10.1109/TWC.2007.05912
M3 - Journal article
VL - 6
SP - 3589
EP - 3599
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 10
ER -