Contact Information-Based Indoor Pedestrian Localization Using Bluetooth Low Energy Beacons

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/nonproximity 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.

Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator

“…However, fusion technologies often require additional devices and technical integration for the system, leading to increased costs and design complexity. Research has also been conducted to improve indoor positioning accuracy using multilateration, calculating the position based on the signals from four or more APs [31]. However, applying multilateration in RSSI-based indoor positioning makes the system more susceptible to noise and introduces the challenge of increased computational complexity during position estimation.…”

“…However, only a few studies present position correction methods. [26] Based on Gaussian distribution Linear regression None Fu et al [27] Thompson Tau test Continuous feature scaling None Ozer and John [28] None Kalman filter None Jinayong et al [29] None Gaussian filter Weighted sliding window Albraheem and Alawad [30] None None Levenberg-Marquardt algorithm Shiraki and Shioda [31] None Referring to previous RSSI None…”

Penalized Maximum-Likelihood-Based Localization for Unknown Number of Targets Using WSNs: Terrestrial and Underwater Environments

“…Due to these features, WSNs have applications in various sectors, including military operations, industrial monitoring and control, health care and medical systems, intelligent transportation systems, smart cities, environmental monitoring, etc. [1]- [3].…”