A Low-Cost Single-Anchor Solution for Indoor Positioning Using BLE and Inertial Sensor Data

Feng Ye,Lixiong Huang,Zuoya Liu,Ruizhi Chen,Xuesheng Peng,Guangyi Guo

doi:10.1109/access.2019.2951281

Abstract

Indoor positioning services have become necessary in many situations. Radio frequency (RF) signals are suitable for being used for positioning because of their ubiquity and imperceptibility. This paper utilizes the information from the baseband of a Bluetooth low energy (BLE) transceiver for angle estimation and signal strength for distance estimation. The scheme constitutes a single-anchor based solution to calculate the position of a client. It significantly reduces the cost of installation by avoiding traditional methods like multilateration or triangulation that require three or more anchors, even in a small space. To improve the performance, we design a fusion algorithm based on a Kalman filter to integrate measurements of the anchor station and simplified pedestrian dead reckoning (PDR) results from the client. Experiments show that the proposed solution estimates positions in high precision without initial user location or heading information. The mean error of the implementation is less than 1 m and can be improved to less than 0.5 m with a precise ranging measurement.

Highlights

Unlike global navigation satellite systems (GNSS) outdoors [1], indoor positioning technologies cannot be widely applied in practice
There are some basic requirements that indoor positioning systems need to satisfy to make indoor localization becoming a ubiquitous service: accuracy is the most important; convenience refers to the service being accessed; inexpensive means the cost must be acceptable for most people
The angle of arrival (AoA) algorithm was processed in a laptop which served as a server

Summary

Introduction

Unlike global navigation satellite systems (GNSS) outdoors [1], indoor positioning technologies cannot be widely applied in practice. Different indoor positioning methods are being researched. Some make the most of existing signals indoors, such as GSM [2], [3], geomagnetic fields [4], [5], and Wi-Fi [6]–[8]. The signals called ‘opportunity signals’ are not originally for positioning, and can be exploited but cannot work precisely [9]–[11]. Special base stations with the abilities of the Internet of Things (IoT) and positioning are preferred. These devices can be deployed in public buildings without any impacts.

Methods

Results

Conclusion