A comparative analysis of indoor localization technologies
Koorosh Roohi, Atena Roshan Fekr
Abstract
ABSTRACT Indoor localization holds great potential in various applications such as healthcare facilities, smart buildings, retail and shopping malls, museums, airports, parking lots, etc. Indoor localization systems aim to track and navigate targets in indoor spaces. These systems use various sets of technologies that can be categorized into four groups: Radio Frequency (RF) based, inertial based, optical based, and ultrasound based. To have a fair comparison between different technologies, in this review paper, we divide these technologies into wearable, contactless, and a fusion of different technology groups. All of these methods are proposed and used with different approaches such as machine learning, deep learning, geometric, and signal processing techniques. In this paper, we compare these methods in terms of localization performance, time complexity, coverage, and generalizability. Also, we determine which of these methods are suitable for different applications. It was observed that methods based on contactless RF based technologies outperformed others by showing centimeter level localization accuracy and preserving users' privacy. Additionally, fusing different types of technology can enhance performance compared to when they are used solely. Technologies and techniques that need further research are also discussed in details.