Robust WiFi-based indoor localization using multipath component analysis

The number of applications that rely on robust indoor localization is constantly growing. Conventional outdoor localization technologies are generally not suited for indoor use. WiFi-based indoor localization systems are a widely studied alternative since the necessary infrastructure is already available in most buildings. Existing WiFi-based localization algorithms, however, still face challenges such as high sensitivity to changes in the environment, temporal instability, a time consuming calibration process and low accuracy in non-line-of-sight (NLOS) multipath environments. In this paper, we propose a novel fingerprinting-based WiFi indoor localization scheme which operates by extracting and analyzing individual multipath propagation delays. We demonstrate through simulation the robustness of the proposed algorithm to changes in the multipath environment.