Paper accepted at the 8th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2017)

Paper details

Ngewi Fet, Marcus Handte und Pedro José Marrón: Autonomous Signal Source Displacement Detection and Recalibration of Fingerprinting-based Indoor Localization Systems


Fingerprinting-based indoor localization systems rely on stable signal distribution characteristics of fixed signal sources for location estimation. However, indoor environments are not static and changes in the environment can lead to displacement of some signal sources, potentially causing a drop in the localization performance. It is therefore necessary to regularly monitor the signal sources and manually recalibrate any whose signal distribution has changed. The effort for calibrating these systems is typically high, especially for large indoor environments. In this paper, we propose an approach for autonomously detecting the displacement of a signal source using only measurements collected by active users of the system. We demonstrate that the approach can reliably detect displaced signal sources as well as multiple simultaneous displacements of up to half of the deployed signals sources. We further show that we can use the same measurements to autonomously recalibrate the (WLAN- or Bluetooth-based) indoor localization system, achieving localization performance  comparable to manual calibration.

8th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2017), September 18-21, 2017, Sapporo, Japan. Find more information here: