An Approach for Autonomous Recalibration of Fingerprinting-Based Indoor Localization Systems

Abstract

Fingerprinting-based indoor localization systems tend to achieve higher accuracy compared to other approaches such as signal propagation modeling. However, they also tend to have a higher effort/cost for deployment and maintenance. Changes in the configuration of the indoor space like moving of furniture, or defective signal sources can cause the signal characteristics in the environment to change significantly, and thereby render the fingerprint radio map (used for training the system) outdated. This leads to a drop in localization performance of the system over time. In this paper, we propose an approach to using the system infrastructure for periodically detecting changes in the signal characteristics and autonomously recalibrating the fingerprint radio map. We demonstrate that we can reliably detect changes in signal characteristics stemming from the dampening of a signal source (e.g. induced by moving of furniture) and recalibrate the localization system with an accuracy of 83% to 93% of the optimum localization performance achievable through manual system recalibration.