State estimation using particle filters in wildfire spread simulation
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
A minimum entropy estimation based mobile positioning algorithm
IEEE Transactions on Wireless Communications
A fast Bayesian model for latent radio signal prediction
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
An efficient Handoff call handling and queuing scheme using location information
WOC '08 Proceedings of the Eighth IASTED International Conference on Wireless and Optical Communications
Bartendr: a practical approach to energy-aware cellular data scheduling
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Assessment of nonlinear dynamic models by Kolmogorov-Smirnov statistics
IEEE Transactions on Signal Processing
Particle filtering: the need for speed
EURASIP Journal on Advances in Signal Processing
Estimation of new ignited fires using particle filters in wildfire spread simulation
Proceedings of the 44th Annual Simulation Symposium
Landmark-assisted location and tracking in outdoor mobile network
Multimedia Tools and Applications
Wireless Personal Communications: An International Journal
Data assimilation using sequential monte carlo methods in wildfire spread simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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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 Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.