Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions
IEEE Transactions on Signal Processing
A simple and efficient estimator for hyperbolic location
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Jump Markov Particle Filter for Localization of Moving Terminals in Multipath Indoor Scenarios
IEEE Transactions on Signal Processing - Part I
Analysis of wireless geolocation in a non-line-of-sight environment
IEEE Transactions on Wireless Communications
The COST259 Directional Channel Model-Part I: Overview and Methodology
IEEE Transactions on Wireless Communications
Mobility Tracking in Cellular Networks Using Particle Filtering
IEEE Transactions on Wireless Communications
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The paper investigates the problem of mobile tracking in mixed line-of-sight (LOS)/non-line-of-sight (NLOS) conditions. The motion of mobile station is modeled by a dynamic white noise acceleration model, while the measurements are time of arrival (TOA). A first-order Markov model is employed to describe the dynamic transition of LOS/NLOS conditions. An improved Rao-Blackwellized particle filter (RBPF) is proposed, in which the LOS/NLOS sight conditions are estimated by particle filtering using the optimal trial distribution, and the mobile state is computed by applying approximated analytical methods. The theoretical error lower bound is further studied in the described problem. A new method is presented to compute the posterior Cramer-Rao lower bound (CRLB): the mobile state is first estimated by decentralized extended Kalman filter (EKF) method, then sigma point set and unscented transformation are applied to calculate Fisher information matrix (FIM). Simulation results show that the improved RBPF is more accurate than current methods, and its performance approaches to the theoretical bound.