Handling Device Heterogeneity in Wi-Fi based Indoor Positioning Systems

Wi-Fi Received Signal Strength (RSS) based indoor localization is promising and widely investigated due to the pervasive deployment of Wi-Fi Access Points (APs). However, one major challenge to build a practical Indoor Positioning System is that end users usually carry different devices with different received signal characteristics, and thus the performance can be degraded due to this device heterogeneity. Existing solutions are either not practical or have limited accuracy. We propose two novel solutions to mitigate device heterogeneity for representative localization approaches using Gaussian Process regression and neural network, respectively. The first solution is built upon Gaussian Process regression by jointly calibrating and localizing a target device. The second solution utilizes adversarial training with neural network. Real world experiments show that both solutions are effective and achieve higher accuracy than that of two baseline approaches in most cases.