Wi-Fi Node Location Estimation Based on GNSS and Motion Sensor Data

Indoor localization is a well researched scientific topic and demanded commercial and technological area. However, the problem of scalability remains for indoor localization systems. Though there is a plenty of radio-based approaches for indoor localization that achieve high level of accuracy, many of those rely on manual data collection which is laborious and not globally scalable. In this paper we approach the problem of scalable radio-mapping by improving estimation of horizontal locations of Wi-Fi radio nodes using GNSS and motion sensor data collected in crowd-sourcing manner, i.e. without manual human intervention. We use simple and yet robust sensor fusion algorithms based on Kalman Filter to estimate pedestrian tracks in indoor and outdoor environments, and then use resulting location estimates as a reference for radio measurements, which are further used to estimate horizontal locations of Wi-Fi radio nodes indoors. We then analyze different radio measurement selection criteria for Wi-Fi node location estimation methods. The experiments based on real data indicate that sensor fusion considerably improves localization of Wi-Fi radio nodes when compared to approaches relying on GNSS data only. Our study also shows that using only radio measurements with strong signal and accurate location reference results in more accurate localization of Wi-Fi radio nodes. The results also indicate that estimation of Wi-Fi radio node locations with accuracy below 15-20 meters on average is achievable without manual data collection, and hence in a globally scalable way. Proposed approaches may be further extended with sensor fusion methods utilizing, for example, misalignment estimation and magnetometer measurements, as well as applied to radio technologies other than Wi-Fi, such as 5G radio technologies.