Mobility Tracking in Cellular Networks Using Particle Filtering.

Mihaylova, L and Angelova, D and Honary, S and Bull, D and Canagarajah, N and Ristic, B (2007) Mobility Tracking in Cellular Networks Using Particle Filtering. IEEE Transactions on Wireless Communications, 6 (10). pp. 3589-3599.

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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.

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Journal Article
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IEEE Transactions on Wireless Communications
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Uncontrolled Keywords:
?? mobility trackingwireless networkshybrid systemssequential monte carlo methodsrao-blackwellisationdcs-publications-idart-865dcs-publications-creditscodingdcs-publications-personnel-id121applied mathematicscomputer science applicationselectrical and electr ??
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10 Dec 2007 08:46
Last Modified:
15 Jul 2024 11:30