Gaussian Processes for Signal Strength-Based Location Estimation
Proc. of Robotics: Science and Systems, 2006
Abstract
Estimating the location of a mobile device or a robot from wireless
signal strength has become an area of highly active research. The
key problem in this context stems from the complexity of how signals
propagate through space, especially in the presence of obstacles
such as buildings, walls or people. In this paper we show how
Gaussian processes can be used to generate a likelihood model for
signal strength measurements. We also show how parameters of the model, such
as signal noise and spatial correlation between measurements, can be
learned from data via hyperparameter estimation. Experiments using
WiFi indoor data and GSM cellphone connectivity demonstrate the
superior performance of our approach.