HILS: hybrid indoor localisation system using Wi-Fi received signal strength and inertial sensor's measurements of smart-phone

• Published in: IET communications 2019-07, Vol.13 (11), p.1595-1606
• Main Authors: Tiwari, Sushil, Jain, Vinod kumar
• Format: Article
• Language: English
• Subjects: benchmark hybrid localisation methods; HILS; indoor radio; inertial sensor measurements; localisation accuracy; mean localisation error; mobile computing; mobile device; novel Heron‐bilateration‐based position determination technique; PDR method; pedestrian dead reckoning; radio propagation model‐based method; radiowave propagation; Research Article; RSSI; smart phones; smart‐phone‐based hybrid indoor localisation system; strong Wi‐Fi signals; wireless LAN; wireless sensor networks; Wi‐Fi received signal strength; Wi‐Fi‐based localisation
• ISSN: 1751-8628; 1751-8636; 1751-8636
• DOI: 10.1049/iet-com.2018.5845

This study presents a smart-phone-based hybrid indoor localisation system (HILS), which integrates two different localisation approaches, i.e., pedestrian dead reckoning (PDR) and Wi-Fi-based localisation to compensate the limits of each other. In PDR, the localisation accuracy degrades continuously when the moving distance increases from the starting reference point. However, Wi-Fi-based trilateration method requires the Wi-Fi signals from at least three access points (APs). In this study, the authors propose a more efficient novel Heron-bilateration-based position determination (HBPD) technique, which requires the Wi-Fi signals from only two APs to locate the mobile device. The proposed HILS utilises the HBPD technique to reset the bias drift errors involved with inertial sensors, and compensate the unavailability of strong Wi-Fi signals from APs using the PDR method. In this work, an experimental testbed is developed to collect the Wi-Fi signals and evaluate the proposed method. The proposed HILS is also compared with five other benchmark methods for two different trajectories and the experimental results show that the mean localisation error is decreased up to 1.68 and 1.96 m, which indicate the improvement by 16.83 and 20.97% for both the trajectories, respectively, in comparison with the best performing method among the benchmark methods.