Mobility Tracking in Cellular Networks Using Particle Filtering

摘要: Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility in communication by means received signal strength indications. These allow accurate estimation mobile station's (MS) position speed. The command process the MS represented first-order Markov model which can take values finite set acceleration levels. wide range changes covered preliminary determined values. A particle filter Rao-Blackwellised are proposed their performance evaluated over synthetic real data. comparison with an extended Kalman (EKF) performed respect to accuracy computational complexity. With small number particles RBPF gives more results than PF EKF. posterior Cramer Rao lower bound (PCRLB) calculated it compared filters' root- mean-square error performance.