ABSTRACT
Indoor localization technologies are actively investigated to realize location-based applications in various environments, and indoor localization methods based on whether the received signal strength indicator (RSSI) is less than a threshold have been proposed previously. Such a proximity/non-proximity binary value is used in digital contact tracing applications to reduce the coronavirus disease effects. We proposed two indoor pedestrian localization methods based on contact information using bluetooth low energy (BLE) beacons, namely multilateration and cooperative localization. This study attempts to demonstrate the effectiveness of the proposed methods using only contact information. Through simulation experiments, we found that the proposed methods can achieve comparable accuracy to existing methods when the attenuation model is accurate. The difference in average localization error was 0.1 m between the proposed method 1 and range-based method, and 0.2 m between the proposed method 2 and fingerprinting method. We confirmed that the proposed methods using only contact information are robust against environmental changes even when the attenuation model is inaccurate. We consider that these contributions have added a new perspective on the use of contact information in the field of indoor localization, which aims to realize power-saving and cost reduction.
ABSTRACT
COVID-19 digital contact tracing applications for smartphones have become popular worldwide to reduce the effects of the pandemic. We considered that contact information between smartphones used in these applications can be used for the indoor localization of pedestrians. In this paper, we propose two indoor pedestrian localization methods based on contact information obtained from Bluetooth low energy (BLE) beacons installed in pedestrian's smartphones. Proposed method 1 is multilateration, and proposed method 2 solves a nonlinear optimization problem to further improve the accuracy of method 1. These two proposed methods comprise three steps: (1) the smartphones and anchor nodes recognize the proximity relationship with neighbor nodes using BLE signals transmitted from other smartphones and anchor nodes. The recognized proximity relationship is sent to a server. (2) The server estimates the distance between each node (smartphone or anchor node) from the proximity relationship. (3) The positions of smartphones are estimated based on the distance between nodes estimated by the server. We verified the localization accuracy of the proposed methods through simulation experiments. In an indoor area of 15 m × 30 m, the average localization error of the proposed method 2 was 0.74 m when the pedestrian density was 0.5 /m2. © 2022 IEEE.