Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy.

Aybars Kerem Taşkan,Hande Alemdar

doi:10.3390/s21030971

Aybars Kerem Taşkan, Hande Alemdar

Open Access

https://doi.org/10.3390/s21030971

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Abstract

Indoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP), a cost-effective BLE-based indoor positioning algorithm. OASLTIP uses a combination of techniques together to provide optimum tracking performance by taking into account the obstructions in the environment, and also, it can handle a loss of signal. We use running average filtering to smooth the received signal data, multilateration to find the measured position of the tag, and particle filtering to track the tag for better performance. We also propose an optional receiver placement method and provide the option to use fingerprinting together with OASLTIP. Moreover, we give insights about BLE signal strengths in different conditions to help with understanding the effects of some environmental conditions on BLE signals. We performed extensive experiments for evaluation of the OASLTool we developed. Additionally, we evaluated the performance of the system both in a simulated environment and in real-world conditions. In a highly crowded and occluded office environment, our system achieved 2.29 m average error, with three receivers. When simulated in OASLTool, the same setup yielded an error of 2.58 m.

Highlights

Indoor positioning systems (IPSs) are useful, especially in large environments such as big malls, stores, museums and similar places [1,2]
We present a novel wireless indoor positioning algorithm called obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP) that has a unique pipeline for Bluetooth Low Energy (BLE)-based indoor tracking that consists of pre-filtering, multilateration and particle filtering-based tracking
We presented the OASLTIP algorithm to perform indoor localization and tracking using BLE tags

Summary

Introduction

Indoor positioning systems (IPSs) are useful, especially in large environments such as big malls, stores, museums and similar places [1,2]. In those environments, what to do and where to go without spending much time on looking for directions can be very difficult. The handicapped and the elderly, indoor navigation can be more difficult [3,4]. Navigation systems that direct people can make use of IPS to work effectively. A museum application can send informative push notifications to the visitors when they look at a particular painting or an antique piece by knowing their positions

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