CDMA: principles of spread spectrum communication
CDMA: principles of spread spectrum communication
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Digital Communications and Spread Spectrum System
Digital Communications and Spread Spectrum System
Monte Carlo Bayesian Signal Processing for Wireless Communications
Journal of VLSI Signal Processing Systems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Joint mobility tracking and handoff in cellular networks via sequential Monte Carlo filtering
IEEE Transactions on Signal Processing
Joint state and parameter estimation for a target-directed nonlinear dynamic system model
IEEE Transactions on Signal Processing
Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Hybrid TDOA/AOA mobile user location for wideband CDMA cellular systems
IEEE Transactions on Wireless Communications
An overview of the challenges and progress in meeting the E-911 requirement for location service
IEEE Communications Magazine
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
We propose a new technique for mobile tracking in wideband code-division multiple-access (WCDMA) systems employing multiple receive antennas. To achieve a high estimation accuracy, the algorithm utilizes the time difference of arrival (TDOA) measurements in the forward link pilot channel, the angle of arrival (AOA) measurements in the reverse-link pilot channel, as well as the received signal strength. The mobility dynamic is modelled by a first-order autoregressive (AR) vector process with an additional discrete state variable as the motion offset, which evolves according to a discrete-time Markov chain. It is assumed that the parameters in this model are unknown and must be jointly estimated by the tracking algorithm. By viewing a nonlinear dynamic system such as a jump-Markov model, we develop an effcient auxiliary particle filtering algorithm to track both the discrete and continuous state variables of this system as well as the associated system parameters. Simulation results are provided to demonstrate the excellent performance of the proposed adaptive mobile positioning algorithm in WCDMA networks.