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Magnetic sensor based indoor positioning by multi-channel deep regression: poster abstract

Published: 16 November 2020 Publication History

Abstract

Modern smartphones are equipped with built-in magnetometers that capture disturbances of the Earth's magnetic field induced by ferromagnetic objects. In indoor environment, using magnetic field data turns to be a strong alternative to conventional localization techniques as requiring no special infrastructure. We revise the state of the art methods based on landmark classification [5] and propose a novel approach. We represent magnetic data time series as image sequences and compose multi-channel input to a deep neural network. We use four methods, Recurrence plots, Gramian Angular Fields and Markov Transition Fields, to capture different patterns in magnetic data stream. We complete the landmark-based classification with deep regression on the user's position and combine convolutional and recurrent layers in the deep network. We evaluate our methods on the recently published MagPie dataset [3] and show that they outperform the state of the art methods.

References

[1]
Imran Ashraf, Soojung Hur, and Yongwan Park. Enhancing performance of magnetic field based indoor localization using magnetic patterns from multiple smartphones. Sensors, 20(9):2704, 2020.
[2]
Fuqiang Gu, Xuke Hu, Milad Ramezani, Debaditya Acharya, Kourosh Khoshelham, Shahrokh Valaee, and Jianga Shang. Indoor localization improved by spatial context --- a survey. ACM Comput. Surv., 52(3):64:1--64:35, July 2019.
[3]
David Hanley, Alexander B. Faustino, Scott D. Zelman, David A. Degenhardt, and Timothy Bretl. MagPIE: a dataset for indoor positioning with magnetic anomalies. Intern. Conf. Indoor Positioning and Indoor Navigation (IPIN), pages 1--8, 2017.
[4]
S. Khoshrou, J.S. Cardoso, and L.F. Teixeira. Learning from evolving video streams in a multi-camera scenario. Machine Learning, 100:609--633, 2015.
[5]
Namkyoung Lee, Sumin Ahn, and Dongsoo Han. AMID: accurate magnetic indoor localization using deep learning. Sensors, 18(5):1598, 2018.
[6]
Zhiguang Wang and Tim Oates. Imaging time-series to improve classification and imputation. Proc. IJCAI, pages 3939--3945, 2015.

Cited By

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  • (2024)BLIPS: Bluetooth locator for an Indoor Positioning System in RealtimeProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675057(18-29)Online publication date: 8-Jul-2024
  • (2023)Using ARIMA to Predict the Growth in the Subscriber Data UsageEng10.3390/eng40100064:1(92-120)Online publication date: 1-Jan-2023
  • (2022)Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 CompetitionIEEE Sensors Journal10.1109/JSEN.2021.308314922:6(5011-5054)Online publication date: 15-Mar-2022
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  1. Magnetic sensor based indoor positioning by multi-channel deep regression: poster abstract

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    cover image ACM Conferences
    SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
    November 2020
    852 pages
    ISBN:9781450375900
    DOI:10.1145/3384419
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 16 November 2020

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    Author Tags

    1. indoor localization
    2. magnetic field sensor
    3. mobile computing

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    View all
    • (2024)BLIPS: Bluetooth locator for an Indoor Positioning System in RealtimeProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675057(18-29)Online publication date: 8-Jul-2024
    • (2023)Using ARIMA to Predict the Growth in the Subscriber Data UsageEng10.3390/eng40100064:1(92-120)Online publication date: 1-Jan-2023
    • (2022)Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 CompetitionIEEE Sensors Journal10.1109/JSEN.2021.308314922:6(5011-5054)Online publication date: 15-Mar-2022
    • (2022)Predicting Subscriber Usage: Analyzing Multidimensional Time-Series Using Convolutional Neural NetworksCyber Security, Cryptology, and Machine Learning10.1007/978-3-031-07689-3_20(259-269)Online publication date: 23-Jun-2022

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