Abstract

Smartphones are becoming more widespread, and location-based services (LBS) have become one of the most important uses in people’s daily lives. While outdoor location is reasonably simple thanks to GNSS signals, however, indoor location is more problematic due to the lack of GNSS signals. As a result of the widespread deployment of alternative technologies such as wireless and sensors technologies, various studies on wireless-based indoor positioning have been conducted. However, each technology has its own limitations including multipath fading of wireless signals causes time-varying received signal strength as well as the accumulated error of the onboard sensors (i.e. sensor drift) resulting in poor localization accuracy. Motivated by these restrictions, this work integrates the applicability of two technologies for indoor positioning that are already available in smartphones by avoiding their limitation. The integration is based on fingerprinting-positioning technique by including magnetometer sensor measurements and WiFi signal strength. Android-based smartphones with low-cost sensors in real indoor scenarios are utilized to create a dataset and collect independent track tests to confirm results. The performance of different scenarios, such as Wi-Fi alone, magnetometer alone, and magnetometer-aided Wi-Fi, is compared. The experimental results show that the combination of magnetometer sensor and WiFi signal strength provides significant results in which leads to reducing the location error to 0.7224 meters.

Highlights

  • Utilizing smartphone applications to run daily life activities via users is increased day-byday

  • Wi-Fi RSS-based fingerprinting location has grown popular, the RSS measurements are not stable due to: 1) WiFi signal interference, WiFi chipset reading errors (i.e. ±4 dBm), and WiFi signal coverage in the vicinity [8]. With inertial sensors such as magnetometer sensor, the inside magnetic field anomalies induced by structural steel elements impact orientation estimate, it is a signature for localization purposes in the meantime [9]

  • This study proposed a new integration of WiFi RSS values with magnetometer sensor measurements into fingerprint positioning techniques

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Summary

Introduction

Utilizing smartphone applications to run daily life activities via users is increased day-byday. The indoor positioning algorithm estimates a location based on currently observed signal strengths and previously gathered information from RPs [5]. The received signal intensity can be modified by diffraction, reflection, scattering, and absorption during propagation in indoor situations, which is the main problem for positioning algorithms based on location fingerprinting, because of the magnetic field's stability and distinctiveness, a number magnetic-based positioning techniques have been developed [6]. With inertial sensors such as magnetometer sensor, the inside magnetic field anomalies induced by structural steel elements impact orientation estimate, it is a signature for localization purposes in the meantime [9] It is clear from the literature review that the majority of indoor positioning tests necessitate additional equipment installation [10,11,12] or they provide low positioning accuracy [13, 14].

The Proposed Algorithm
Dataset and Experimental Setup
Results and Discussions
Conclusion
Full Text
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