Large-Scale Localization from Wireless Signal Strength
Proc. of the National Conference on
Artificial Intelligence (AAAI-05).
Abstract
Knowledge of the physical locations of mobile devices such as
laptops or PDA's is becoming increasingly important with the rise of
location-based services such as specialized web search, navigation,
and social network applications; furthermore, location information
is a key foundation for high-level activity inferencing. In this
paper we propose a novel technique for accurately estimating the
locations of mobile devices and their wearers from wireless signal
strengths. Our technique estimates time-varying device locations on
a spatial connectivity graph whose outdoor edges correspond to
streets and whose indoor edges represent hallways, staircases,
elevators, \emph{etc}. Use of a hierarchical Bayesian framework for
learning a signal strength sensor model allows us not only to
achieve higher accuracy than existing approaches, but to overcome
many of their limitations. In particular, our technique is able to
(1) seamlessly integrate new access points into the model, (2) make
use of negative information (not detecting an access point), and (3)
bootstrap a sensor model from sparse training data. Experiments
demonstrate various properties of our system.