ArticleSensors (Basel, Switzerland)2020
Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Axes Mapping and Sensor Fusion for Attitude-Unconstrained Pedestrian Dead Reckoning.Sensors (Basel, Switzerland) · 2026Article
- Indoor Localization Methods for Smartphones with Multi-Source Sensors Fusion: Tasks, Challenges, Strategies, and Perspectives.Sensors (Basel, Switzerland) · 2025Review
- Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI).Sensors (Basel, Switzerland) · 2022Article
- Analysis of Magnetic Field Measurements for Indoor Positioning.Sensors (Basel, Switzerland) · 2022Article
- Effectiveness of Artificial Neural Networks for Solving Inverse Problems in Magnetic Field-Based Localization.Sensors (Basel, Switzerland) · 2022Article
- Wi-Fi Access Point Design Concept Targeting Indoor Positioning for Smartphones and IoT.Sensors (Basel, Switzerland) · 2022Article
- Empirical Overview of Benchmark Datasets for Geomagnetic Field-Based Indoor Positioning.Sensors (Basel, Switzerland) · 2021Article
- Mass Tracking in Cellular Networks for the COVID-19 Pandemic Monitoring.Sensors (Basel, Switzerland) · 2021Article
- An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
Wide expansion of smartphones triggered a rapid demand for precise localization that can meet the requirements of location-based services. Although the global positioning system is widely used for outdoor positioning, it cannot provide the same accuracy for the indoor. As a result, many alternative indoor positioning technologies like Wi-Fi, Bluetooth Low Energy (BLE), and geomagnetic field localization have been investigated during the last few years. Today smartphones possess a rich variety of embedded sensors like accelerometer, gyroscope, and magnetometer that can facilitate estimating the current location of the user. Traditional geomagnetic field-based fingerprint localization, although it shows promising results, it is limited by the fact that various smartphones have embedded magnetic sensors from different manufacturers and the magnetic field strength that is measured from these smartphones vary significantly. Consequently, the localization performance from various smartphones is different even when the same localization approach is used. So devising an approach that can provide similar performance with various smartphones is a big challenge. Contrary to previous works that build the fingerprint database from the geomagnetic field data of a single smartphone, this study proposes using the geomagnetic field data collected from multiple smartphones to make the geomagnetic field pattern (MP) database. Many experiments are carried out to analyze the performance of the proposed approach with various smartphones. Additionally, a lightweight threshold technique is proposed that can detect user motion using the acceleration data. Results demonstrate that the localization performance for four different smartphones is almost identical when tested with the database made using the magnetic field data from multiple smartphones than that of which considers the magnetic field data from only one smartphone. Moreover, the performance comparison with previous research indicates that the overall performance of smartphones is improved.
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