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Accurate Indoor Localization Using Magnetic Sequence Fingerprints with Deep Learning

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Algorithms and Architectures for Parallel Processing (ICA3PP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13155))

Abstract

Magnetic field fingerprinting has been an interesting topic in indoor localization researches because of its advantages of being ubiquitous, energy-efficient and infrastructure-free. Most existing indoor magnetic field-based positioning methods use the raw three-dimensional magnetic field strength obtained by the magnetic sensor built in smartphones. However, they have to overcome the problem of ambiguity that originates from the nature of geomagnetic data, especially in the large-scale environment. In this paper, we first expand the dimension of magnetic data elements, and a sliding window mechanism is designed to construct magnetic sequence fingerprints to increase the distinguishability of magnetic field fingerprints. Moreover, an accurate indoor positioning model combining the advantages of one-dimensional convolutional neural network and long short-term memory network is designed to automatically learn the mapping between ground-truth positions and magnetic sequence fingerprints. To demonstrate the effectiveness of our proposed method, we perform a comprehensive experimental evaluation on three real-world datasets, and the results show that the proposed approach can remarkably improve positioning performance compared with other methods.

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Notes

  1. 1.

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References

  1. Abbas, M., Elhamshary, M., Rizk, H., Torki, M., Youssef, M.: Wideep: Wifi-based accurate and robust indoor localization system using deep learning. In: 2019 IEEE International Conference on Pervasive Computing and Communications, PerCom, Kyoto, Japan, 11–15 March 2019, pp. 1–10. IEEE (2019). https://doi.org/10.1109/PERCOM.2019.8767421

  2. Ashraf, I., Kang, M., Hur, S., Park, Y.: MINLOC: magnetic field patterns-based indoor localization using convolutional neural networks. IEEE Access 8, 66213–66227 (2020). https://doi.org/10.1109/ACCESS.2020.2985384

    Article  Google Scholar 

  3. Chen, Y., Zhou, M., Zheng, Z.: Learning sequence-based fingerprint for magnetic indoor positioning system. IEEE Access 7, 163231–163244 (2019). https://doi.org/10.1109/ACCESS.2019.2952564

    Article  Google Scholar 

  4. Chiang, T.H., Sun, Z.H., Shiu, H.R., Lin, K.C.J., Tseng, Y.C.: Magnetic field-based localization in factories using neural network with robotic sampling. IEEE Sensors J. 20(21), 13110–13118 (2020). https://doi.org/10.1109/JSEN.2020.3003404

    Article  Google Scholar 

  5. Chung, J., Donahoe, M., Schmandt, C., Kim, I., Razavai, P., Wiseman, M.: Indoor location sensing using geo-magnetism. In: Agrawala, A.K., Corner, M.D., Wetherall, D. (eds.) Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys 2011), Bethesda, MD, USA, 28 June–01 July 2011, pp. 141–154. ACM (2011). https://doi.org/10.1145/1999995.2000010

  6. Frassl, M., Angermann, M., Lichtenstern, M., Robertson, P., Julian, B.J., Doniec, M.: Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 3–7 November 2013, pp. 913–920. IEEE (2013). https://doi.org/10.1109/IROS.2013.6696459

  7. Galván-Tejada, C.E., García-Vázquez, J., Brena, R.F.: Magnetic field feature extraction and selection for indoor location estimation. Sensors 14(6), 11001–11015 (2014). https://doi.org/10.3390/s140611001

    Article  Google Scholar 

  8. Grand, E.L., Thrun, S.: 3-axis magnetic field mapping and fusion for indoor localization. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2012, Hamburg, Germany, 13–15 September 2012, pp. 358–364. IEEE (2012). https://doi.org/10.1109/MFI.2012.6343024

  9. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  10. Lee, N., Ahn, S., Han, D.: AMID: accurate magnetic indoor localization using deep learning. Sensors 18(5), 1598 (2018). https://doi.org/10.3390/s18051598

    Article  Google Scholar 

  11. Li, T., Wang, H., Shao, Y., Niu, Q.: Channel state information-based multi-level fingerprinting for indoor localization with deep learning. Int. J. Distrib. Sens. Netw. 14(10) (2018). https://doi.org/10.1177/1550147718806719

  12. Pusnik, M., Galun, M., Sumak, B.: Improved bluetooth low energy sensor detection for indoor localization services. Sensors 20(8), 2336 (2020). https://doi.org/10.3390/s20082336

    Article  Google Scholar 

  13. Shao, W., et al.: Location fingerprint extraction for magnetic field magnitude based indoor positioning. J. Sensors 2016, 1945695:1–1945695:16 (2016). https://doi.org/10.1155/2016/1945695

  14. Suksakulchai, S., Thongchai, S., Wilkes, D.M., Kawamura, K.: Mobile robot localization using an electronic compass for corridor environment. In: Proceedings of the IEEE International Conference on Systems, Man & Cybernetics: “Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions”, Sheraton Music City Hotel, Nashville, Tennessee, USA, 8–11 October 2000, pp. 3354–3359. IEEE (2000). https://doi.org/10.1109/ICSMC.2000.886523

  15. Wang, R., Li, Z., Luo, H., Zhao, F., Shao, W., Wang, Q.: A robust wi-fi fingerprint positioning algorithm using stacked denoising autoencoder and multi-layer perceptron. Remote Sens. 11(11), 1293 (2019). https://doi.org/10.3390/rs11111293

    Article  Google Scholar 

  16. Wang, X., Gao, L., Mao, S., Pandey, S.: Deepfi: deep learning for indoor fingerprinting using channel state information. In: 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015, New Orleans, LA, USA, 9–12 March 2015, pp. 1666–1671. IEEE (2015). https://doi.org/10.1109/WCNC.2015.7127718

  17. Wang, X., Wang, X., Mao, S.: Cifi: deep convolutional neural networks for indoor localization with 5 ghz wi-fi. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6 (2017). https://doi.org/10.1109/ICC.2017.7997235

  18. Wang, X., Wang, X., Mao, S.: RF sensing in the internet of things: a general deep learning framework. IEEE Commun. Mag. 56(9), 62–67 (2018). https://doi.org/10.1109/MCOM.2018.1701277

    Article  Google Scholar 

  19. Wang, X., Yu, Z., Mao, S.: Indoor localization using smartphone magnetic and light sensors: a deep LSTM approach. Mob. Netw. Appl. 25(2), 819–832 (2020). https://doi.org/10.1007/s11036-019-01302-x

    Article  Google Scholar 

  20. Xiao, C., Yang, D., Chen, Z., Tan, G.: 3-D BLE indoor localization based on denoising autoencoder. IEEE Access 5, 12751–12760 (2017). https://doi.org/10.1109/ACCESS.2017.2720164

    Article  Google Scholar 

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Acknowledgement

This work is supported by the National Key Research and Development Program of China (2020YFB2104202).

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Authors and Affiliations

  1. MOE Engineering Research Center for Software/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, 200062, China

    Xuedong Ding, Minghua Zhu & Bo Xiao

Authors
  1. Xuedong Ding
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  2. Minghua Zhu
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  3. Bo Xiao
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Corresponding authors

Correspondence to Minghua Zhu or Bo Xiao .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Yongxuan Lai

  2. Beijing Normal University, Zhuhai, China

    Tian Wang

  3. Xiamen University, Xiamen, China

    Min Jiang

  4. Tianjin University, Tianjin, China

    Guangquan Xu

  5. Hunan University, Changsha, China

    Wei Liang

  6. University of Naples Parthenope, Naples, Italy

    Aniello Castiglione

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Ding, X., Zhu, M., Xiao, B. (2022). Accurate Indoor Localization Using Magnetic Sequence Fingerprints with Deep Learning. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-95384-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95383-6

  • Online ISBN: 978-3-030-95384-3

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