Indoor Smartphone Localization: A Hybrid WiFi RTT-RSS Ranging Approach

Guangyi Guo,Zuoya Liu,Yuanjin Pan,Xuesheng Peng,Ruizhi Chen,Feng Ye

doi:10.1109/access.2019.2957753

Abstract

Real-time locating and tracking Technology plays a significant role in location-based IoT applications. With the extensive installation of WiFi access points, the WiFi based indoor positioning approach has become one of the most widely used location technologies. However, due to the limitations of wireless signals, the classic WiFi-based method has become labor-intensive. Recently, the WiFi-based two-way ranging approach was introduced into the 802.11-REVmc2 protocol, which is built on a new packet type known as fine timing measurement (FTM) frame. In this work, we introduce the round-trip time measurement with clock skew and analyze the distribution of the round trip time (RTT) ranging error. A calibration method is presented to eliminate the RTT range offset at the transmitter end. We also develop an integrated ranging algorithm based on the WiFi round trip time range and received signal strength to enhance the scalability and robustness of the positioning system. The experimental results demonstrate that the proposed fusion method achieves remarkable improvement in scalability and precision in both static and dynamic tests, including outdoor and indoor environments. Compared with the classic fingerprinting approach, the performance of the system is remarkably improved, and achieves an average positioning accuracy of 1.435 m with an update rate of every 0.19 s.

Highlights

With billions of units in everyday use throughout the world, the smartphone has become a highly popular personal communication, ubiquitous computing, and entertainment platform
We aim to develop an integrated ranging algorithm based on the WiFi round trip time range and received signal strength to enhance the scalability and robustness of the positioning system
The results confirm that the position accuracy, robustness and update rate of the system were notably improved in the real indoor environment and achieved an average accuracy of 1.435 m with an update rate of every 0.19 s

Summary

Introduction

With billions of units in everyday use throughout the world, the smartphone has become a highly popular personal communication, ubiquitous computing, and entertainment platform. The RFbased methods rely on wireless communication signals to locate and track the smartphone, including cellular [1]–[4], Wi-Fi [5]–[22], Bluetooth Low Energy (BLE) [23]–[26], Radio Frequency Identification (RFID) [27]–[29]. These approaches are based on different observations such as signal coverage, received signal strength (RSS), range (via time of arrival or round-trip time), range difference (via time difference of arrival) and direction (via angle of arrival).

Objectives

Findings

Discussion

Conclusion