Crowdsource Based Indoor Localization by Uncalibrated Heterogeneous Wi-Fi Devices

Abstract

Many indoor localization techniques that rely on received signals from Wi-Fi access points have been explored in the last decade. Recently, crowdsourced Wi-Fi fingerprint attracts much attention, which leads to a self-organized localization system avoiding painful survey efforts. However, this participatory approach introduces new challenges with no previously proposed techniques such as heterogeneous devices, short measurement time, and multiple values for a single position. This paper proposes an efficient localization method combating the three major technical issues in the crowdsourcing based systems. We evaluate our indoor positioning method using 5 places with different radio environment and 8 different mobile phones. The experimental results show that the proposed approach provides consistent localization accuracy and outperforms existing localization algorithms.