Position and velocity tracking in mobile networks using particle and Kalman filtering with comparison

Mohammed M. Olama, Seddik M. Djouadi, Ioannis G. Papageorgiou, Charalambos D. Charalambous

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

This paper presents several methods based on signal strength and wave scattering models for tracking a user. The received-signal level method is first used in combination with maximum likelihood (ML) estimation and triangulation to obtain an estimate of the location of the mobile. Due to nonline-of-sight conditions and multipath propagation environments, this estimate lacks acceptable accuracy for demanding services, as the numerical results reveal. The 3-D wave scattering multipath channel model of Aulin is employed, together with the recursive nonlinear Bayesian estimation algorithms to obtain improved location estimates with high accuracy. Several Bayesian estimation algorithms are considered, such as the extended Kalman filter (EKF), the particle filter (PF), and the unscented PF (UPF). These algorithms cope with nonlinearities in order to estimate mobile location and velocity. Since the EKF is very sensitive to the initial state, we propose the use of the ML estimate as the initial state of the EKF. In contrast to the EKF tracking approach, the PF and UPF approaches do not rely on linearized motion models, measurement relations, and Gaussian assumptions. Numerical results are presented to evaluate the performance of the proposed algorithms when the measurement data do not correspond to the ones generated by the model. This shows the robustness of the algorithm based on modeling inaccuracies.

Original languageEnglish
Pages (from-to)1001-1010
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume57
Issue number2
DOIs
StatePublished - Mar 2008
Externally publishedYes

Keywords

  • Kalman filtering
  • Location tracking
  • Maximum likelihood estimation (MLE)
  • Multipath fading channels
  • Particle filtering

Fingerprint

Dive into the research topics of 'Position and velocity tracking in mobile networks using particle and Kalman filtering with comparison'. Together they form a unique fingerprint.

Cite this