Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Adaptive Temporal Radio Maps for Indoor Location Estimation
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
Error Correction Coding: Mathematical Methods and Algorithms
Error Correction Coding: Mathematical Methods and Algorithms
Global Positioning: Technologies and Performance (Wiley Survival Guides in Engineering and Science)
Global Positioning: Technologies and Performance (Wiley Survival Guides in Engineering and Science)
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Design of an adaptive positioning system based on WiFi radio signals
Computer Communications
Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
WiGEM: a learning-based approach for indoor localization
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
The WiMap: A Dynamic Indoor WLAN Localization System
International Journal of Advanced Pervasive and Ubiquitous Computing
An Indoor Localization System within an IMS Service Infrastructure
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Locating schemes based on adaptive weighting strategy in heterogeneous wireless networks
International Journal of Ad Hoc and Ubiquitous Computing
Locating schemes based on adaptive weighting strategy in heterogeneous wireless networks
International Journal of Ad Hoc and Ubiquitous Computing
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Indoor localization of a mobile user can be performed by using the off-the-shelf 802.11 (WiFi) infrastructure. However most of the existing position estimators are based on a stationary environment assumption that turns out to be rarely true in practice. We analyze two different approaches for the simultaneous estimation of the position and of the signal statistical model. The first uses a discrete state approach and is based on the Expectation-Maximization (EM) algorithm; the second employs a continuous state space and Kalman or Particle Filtering methodology. Numerical simulations and implementation show the effectiveness of the latter for real-time applications in nonstationary environments.