Mobility Tracking in Cellular Networks Using Particle Filtering

@article{citeulike:2716738, abstract = {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 Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.}, author = {Mihaylova, L. and Angelova, D. and Honary, S. and Bull, D. R. and Canagarajah, C. N. and Ristic, B. }, booktitle = {Wireless Communications, IEEE Transactions on}, citeulike-article-id = {2716738}, doi = {10.1109/TWC.2007.05912}, journal = {Wireless Communications, IEEE Transactions on}, keywords = {cellular-networks, mobility}, number = {10}, pages = {3589--3599}, posted-at = {2008-04-25 06:11:00}, priority = {2}, title = {Mobility Tracking in Cellular Networks Using Particle Filtering}, url = {http://dx.doi.org/10.1109/TWC.2007.05912}, volume = {6}, year = {2007} }

TY - JOUR ID - citeulike:2716738 L3 - citeulike-article-id:2716738 TI - Mobility Tracking in Cellular Networks Using Particle Filtering T2 - Wireless Communications, IEEE Transactions on JF - Wireless Communications, IEEE Transactions on VL - 6 IS - 10 SP - 3589 EP - 3599 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 Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance. KW - cellular-networks KW - mobility AU - Mihaylova, L AU - Angelova, D AU - Honary, S AU - Bull, DR AU - Canagarajah, CN AU - Ristic, B PY - 2007/// UR - http://dx.doi.org/10.1109/TWC.2007.05912 ER -