Skip to main content

Improving UWB Indoor Localization Accuracy Using Sparse Fingerprinting and Transfer Learning

  • Conference paper
  • First Online:
Computational Science – ICCS 2021 (ICCS 2021)

Abstract

Indoor localization systems become more and more popular. Several technologies are intensively studied with application to high precision object localization in such environments. Ultra-wideband (UWB) is one of the most promising, as it combines relatively low cost and high localization accuracy, especially compared to Beacon or WiFi. Nevertheless, we noticed that leading UWB systems’ accuracy is far below values declared in the documentation. To improve it, we proposed a transfer learning approach, which combines high localization accuracy with low fingerprinting complexity. We perform very precise fingerprinting in a controlled environment to learn the neural network. When the system is deployed in a new localization, full fingerprinting is not necessary. We demonstrate that thanks to the transfer learning, high localization accuracy can be maintained when only 7% of fingerprinting samples from a new localization are used to update the neural network, which is very important in practical applications. It is also worth noticing that our approach can be easily extended to other localization technologies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mautz, R.: Indoor positioning technologies, Habilitation Thesis submitted to ETH Zurich Application for Venia Legend in Positioning and Engineering Geodesy, Institute of Geodesy and Photogrammetry, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich (2012)

    Google Scholar 

  2. Subedi, S., Pyun, Y.: Practical fingerprinting localization for indoor positioning system by using beacons. J. Sens. 2017, Article ID 9742170 (2017)

    Google Scholar 

  3. Zarchan, P., Musoff, H.: Fundamentals of Kalman Filtering: A Practical Approach. American Institute of Aeronautics and Astronautics, Incorporated (2000). ISBN 978-1-56347-455-2

    Google Scholar 

  4. Zand, G., Taherkhani, M., Safabakhsh, R.: Exponential Natural Particle Filter (2015). arXiv:1511.06603

  5. Pozyx Homepage. https://www.pozyx.io, Accessed 11 Jan 2021

  6. Zebra UWB Homepage. https://www.zebra.com/pl/pl.html, Accessed 11 Jan 2021

  7. BeSpoon Homepage. https://ubisense.com/dimension4/, Accessed 11 Jan 2021

  8. Ubisense HomePage. https://www.decawave.com, Accessed 11 Jan 2021

  9. NXP’s automotive UWB Homepage. http://bespoon.com/shop/en/3-products, Accessed 11 Jan 2021

  10. Mimoune, K.M., Ahriz, I., Guillory, J.: Evaluation and improvement of localization algorithms based on UWB pozyx system. In: 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (2019)

    Google Scholar 

  11. DecaWave Homepage, Product Information EVB1000. Overview of EVB1000 Evaluation Board (2013). https://www.decawave.com/product/evk1000-evaluation-kit/, Accessed 11 Jan 2021

  12. Ruiz, A.R.J., Granja, F.S.: Comparing Ubisense, BeSpoon, and DecaWave UWB location systems. In: Indoor Performance Analysis IEEE Transactions on Instrumentation and Measurement. IEEE (2017).

    Google Scholar 

  13. Dabove, P., Di Pietra, V., Piras, M., Jabbar, A.A., Kazim, S.A.:Indoor positioning using Ultra-wide band (UWB) technologies: positioning accuracies and sensors’ performances. In: IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA (2018)

    Google Scholar 

  14. Suining He, S., Gary Chan H.: WiFi fingerprint-based indoor positioning: recent advances and comparisons. In: IEEE Communications Surveys & Tutorials Year. IEEE (2016)

    Google Scholar 

  15. Dunn, P.F., Davis, M.P.: Measurement and Data Analysis for Engineering and Science. CRC Press, Boca Raton (2017). ISBN 9781138050860

    Google Scholar 

  16. Djosic, S., Stojanovic, I., Jovanoic, M., Nikolic, T., Djrdjevic, G.: Fingerprinting-assisted UWB-based localization technique for complex indoor environments. University of Nis, Faculty of Electronic Engineering (2020). https://doi.org/10.1016/j.eswa.2020.114188

  17. Cai, Y., Rai, S.K., Yu, H.: Indoor positioning by distributed machine-learning based data analytics on smart gateway network. In: 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (2020)

    Google Scholar 

  18. Banitaan, S., Azzeh, M., Nassif, S.K.: User movement prediction: the contribution of machine learning techniques. In: 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (2016)

    Google Scholar 

  19. Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings IEEE INFOCOM 2000, Conference on Computer Communications, Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (2020)

    Google Scholar 

  20. Bisio, I., Lavagetto, F., Marchese, M., Sciarrone, A.: Performance comparison of a probabilistic fingerprint-based indoor positioning system over different smartphones, In: 2013 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), (2013).

    Google Scholar 

  21. Sang, C.L., Steinhagen, B., Homburg, J.D., Adams, M., Hesse, M., Rückert, U.: Identification of NLOS and multi-path conditions in UWB localization using machine learning methods. In: Applied Science 2020 (2020). https://doi.org/10.3390/app10113980

  22. Chai, X., Yang, Q.: Reducing the calibration effort for probabilistic indoor location estimation. IEEE Trans. Mob. Comput. 6(6), 649–662 (2007)

    Article  Google Scholar 

  23. Chintalapudi, K.I., Venkata, A.P.: Indoor localization without the pain. In: Proceedings of the Annual International Conference on Mobile Computing and Networking, (MOBICOM) (2010)

    Google Scholar 

  24. IEE Colloquium on Kalman Filters: Introduction, Applications and Future Developments (Digest No. 27). In: IEE Colloquium on Kalman Filters: Introduction, Applications and Future Developments (1989)

    Google Scholar 

  25. Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods, 2nd ed. Springer, New York (2009).

    Google Scholar 

  26. Taneja, S., Gupta, C., Goyal, K., Gureja, D.: An enhanced k-nearest neighbor algorithm using information gain and clustering. In: 2014 Fourth International Conference on Advanced Computing & Communication Technologies, Rohtak (2014)

    Google Scholar 

  27. Sreenivasulu, P., Sarada, J., Dhanesh G.K.: An accurate UWB based localization system using modified leading edge detection algorithm. In: Ad Hoc Networks, vol. 97 (2020)

    Google Scholar 

  28. Mimoune, K., Ahriz, I., Guillory, J.: Evaluation and improvement of localization algorithms based on UWB pozyx system. In: 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia (2019)

    Google Scholar 

  29. Test Uncertainty Performance Test Codes, The American Society of Mechanical Engineers, An American Nationa Standard ASME PTC 19.1–2013 (2013)

    Google Scholar 

  30. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)

    Article  MathSciNet  Google Scholar 

  31. Porti F., Sangjoon, P., Ruiz, A.R., Barsocchi, P.: Comparing the performance of indoor localization systems through the EvAAL framework. In: Sensors 2017, vol. 17 (2017)

    Google Scholar 

  32. D’Aloia, M., et al.: IoT indoor localization with AI technique. In: 2020 IEEE Interna- Workshop on Metrology for Industry 4.0 & IoT, Roma, Italy (2020)

    Google Scholar 

  33. Zhang, W., Sengupta, R., Fodero, J., Li, X.: DeepPositioning: intelligent fusion of pervasive magnetic field and WiFi fingerprinting for smartphone indoor localization via deep learning. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun (2017)

    Google Scholar 

  34. Bai, X., Huang, M., Prasad, N.R., Mihovska, A.D.: A survey of image-based indoor localization using deep learning. In: 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC), Lisbon, Portugal (2019)

    Google Scholar 

  35. Glonek, G., Wojciechowski A.: Kinect and IMU sensors imprecisions compensation method for human limbs tracking. In: International Conference on Computer Vision and Graphics, ICCVG 2016, Poland (2016)

    Google Scholar 

  36. Daszuta, M., Szajerman, D., Napieralski, P.: New emotional model environment for navigation in a virtual reality. Open Phys. 18(1) (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Krzysztof Lichy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Adamkiewicz, K., Koch, P., Morawska, B., Lipiński, P., Lichy, K., Leplawy, M. (2021). Improving UWB Indoor Localization Accuracy Using Sparse Fingerprinting and Transfer Learning. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77980-1_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77979-5

  • Online ISBN: 978-3-030-77980-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics