Automated WLAN calibration with a backtracking particle filter

Location information is one of the most important information sources in ubiquitous computing scenarios. However, a cheap and global indoor positioning solution offering a sufficiently high accuracy and precision for most ubiquitous computing applications without much calibration effort is not yet available. In this paper we present a backtracking particle filter for sensor fusion of accelerometer, magnetometer and WLAN signal strength measurements on a smartphone, offering high indoor tracking accuracy and precision. Even more, we show that backtracking leads to high quality track information where no WLAN is available. This track information is sufficiently accurate to provide for automated calibration or even the creation of a complete new WLAN fingerprint database. A ground truth position is not needed at all. The particle filter and the WLAN calibration technique are evaluated with high quality ground truth in a test environment at our site and the feasibility of the algorithms is demonstrated.