Enhancing Location Estimation through Data Fusion

In this paper we discuss a system that exploits observations derived from sensors, in order to estimate a key factor for pervasive computing and context aware applications: the location of a user. The term "sensors" includes Wi-Fi adapters, IR receivers, etc. The core of the system is the fusion engine which is based on dynamic Bayesian networks (DBNs), a powerful mathematical tool for integrating heterogeneous sensor observations. In closing, is provided an evaluation of the system as it comes out from the experimental results