Journal ArticleDOI
A Probabilistic Approach to WLAN User Location Estimation
TLDR
The feasibility of this approach is demonstrated by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.Abstract:
We estimate the location of a WLAN user based on radio signal strength measurements performed by the user’s mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.read more
Citations
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Journal ArticleDOI
Survey of Wireless Indoor Positioning Techniques and Systems
TL;DR: Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
Proceedings ArticleDOI
The Horus WLAN location determination system
TL;DR: The Horus system identifies different causes for the wireless channel variations and addresses them and uses location-clustering techniques to reduce the computational requirements of the algorithm and the lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.
Journal ArticleDOI
Network-based wireless location: challenges faced in developing techniques for accurate wireless location information
TL;DR: An overview of wireless location challenges and techniques with a special focus on network-based technologies and applications is provided.
Proceedings ArticleDOI
WLAN location determination via clustering and probability distributions
TL;DR: The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients.
Proceedings ArticleDOI
Practical robust localization over large-scale 802.11 wireless networks
Andreas Haeberlen,Eliot John Flannery,Andrew M. Ladd,Algis Rudys,Dan S. Wallach,Lydia E. Kavraki +5 more
TL;DR: The system is sufficiently robust to enable a variety of location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures, and can be adapted to work with previously unknown user hardware.
References
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