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Static and Dynamic Comparison of Pozyx and DecaWave UWB Indoor Localization Systems with Possible Improvements

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Computational Science – ICCS 2021 (ICCS 2021)
  • The original version of this chapter was revised: the surname of the first author in reference 34 was incorrect. This has been corrected. The correction to this chapter is available at https://doi.org/10.1007/978-3-030-77970-2_51

Abstract

This paper investigates static and dynamic localization accuracy of two indoor localization systems using Ultra-wideband (UWB) technology: Pozyx and DecaWave DW1000. We present the results of laboratory research, which demonstrates how those two UWB systems behave in practice. Our research involves static and dynamic tests. A static test was performed in the laboratory using the different relative positions of anchors and the tag. For a dynamic test, we used a robot that was following the EvAAL-based track located between anchors. Our research revealed that both systems perform below our expectations, and the accuracy of both systems is worse than declared by the system manufacturers. The imperfections are especially apparent in the case of dynamic measurements. Therefore, we proposed a set of filters that allow for the improvement of localization accuracy.

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Change history

  • 09 June 2021

    Chapter 18, “Modelling and Forecasting Based on Recurrent Pseudoinverse Matrices” was previously published non-open access. This have now been changed to open access under a CC BY 4.0 license and the copyright holders updated to ‘The Author(s)’ and the acknowledgement section added. The book has also been updated with this change.

    In chapter 44, in reference 34, the surname of the first author was incorrect. The surname has been corrected from “Porti” to “Potortì.”

References

  1. Mautz, R.: Indoor positioning technologies, Habilitation Thesis, Institute of Geodesy and Photogrammetry, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich (2012)

    Google Scholar 

  2. Obreja, S.G., Vulpe, A.: Evaluation of an indoor localization solution based on bluetooth low energy beacons. In: 2020 13th International Conference on Communications (COMM), Bucharest, Romania, pp. 227–231 (2020)

    Google Scholar 

  3. Xue, J., Liu, J., Sheng, M., Shi, Y., Li, J.: A WiFi fingerprint based high-adaptability indoor localization via machine learning. China Commun. 17(7), 247–259 (2020)

    Google Scholar 

  4. Che, F., Ahmed, A., Ahmed, S.G., Zaidi, R., Shakir, M.Z.: Machine learning based approach for indoor localization using Ultra-Wide Bandwidth (UWB) system for Industrial Internet of Things (IIoT). In: 2020 International Conference on UK-China Emerging Technologies (UCET), Glasgow, United Kingdom (2020)

    Google Scholar 

  5. Barbour, N.M., Stark Draper, C.: Inertial Navigation Sensors, Laboratory (P-4994), Cambridge, MA 02139, USA (2011)

    Google Scholar 

  6. Lam, E.W., Little, T.D.C.: Indoor 3D localization with low-cost lifi components. In: 2019 Global LIFI Congress (GLC), Paris, France, pp. 1–6 (2019)

    Google Scholar 

  7. Opromolla, R., Fasano, G., Rufino, G., Grassi, M., Savvaris, A.: LIDAR-inertial integration for UAV localization and mapping in complex environments. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 649–656. Arlington, VA (2016)

    Google Scholar 

  8. Taira, H., et al.: InLoc: indoor visual localization with dense matching and view synthesis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7199–7209 (2018)

    Google Scholar 

  9. Zimmerman, T., Zimmermann, A.: Magic Quadrant for Indoor Location Services, Global Published 13 January 2020 - ID G00385050 (2020)

    Google Scholar 

  10. Zhang, W., Zhu, X., Zhao, Z., Liu, Y., Yang, S.: High accuracy positioning system based on multistation UWB time-of-flight measurements. In: 2020 IEEE International Conference on Computational Electromagnetics (ICCEM), Singapore (2020)

    Google Scholar 

  11. Decawave, APS011 Application Note, Sources of Error in DW1000 Based Two-Way Ranging (TWR) Schemes (2014)

    Google Scholar 

  12. Asmaa, L., Hatim, K.A., Abdelaaziz, M.: Localization algorithms research in wireless sensor network based on multilateration and trilateration techniques. In: 2014 Third IEEE International Colloquium in Information Science and Technology (CIST), Tetouan, pp. 415–419 (2014)

    Google Scholar 

  13. Pozyx Homepage. https://www.pozyx.io. Accessed 01 Feb 2021

  14. Decawave DW1000 product homepage. https://www.decawave.com/product/dw1000-radio-ic/. Accessed 01 Feb 2021

  15. Zebra Homepage. https://www.zebra.com/us/en/products/location-technologies/ultra-wideband.html. Accessed 01 Feb 2021

  16. Ubisense Home Site. https://ubisense.com/dimension4/. Accessed 01 Feb 2021

  17. BeeSpoon Mek 1 Product Homepage. https://bespoon.xyz/produit/mek1-ultra-wideband-module-evaluation-kit/. Accessed 01 Feb 2021

  18. NXP Homepage. https://www.nxp.com/applications/enabling-technologies/connectivity/ultra-widebanduwb:UWB. Accessed 01 Feb 2021

  19. Decawave, APS006 Application Note Channel effects on communications range and time stamp accuracy in DW1000 based systems. https://www.decawave.com/wpcontent/uploads/2018/10/APH001_DW1000-HW-Design-Guide_v1.1.pdf. Accessed 01 Feb 2021

  20. 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 

  21. Daszuta, M., Szajerman, D., Napieralski, P.: New emotional model environment for navigation in a virtual reality. Open Phys. 18(1), 864–870 (2020)

    Google Scholar 

  22. Zhao, Y., Li, Z., Hao, B., Wan, P., Wang, L.: How to select the best sensors for TDOA and TDOA/AOA localization? China Commun. 16(2), 134–145 (2019)

    Google Scholar 

  23. Sinha, P., Yapici, Y., Guvenc, I.: Impact of 3D antenna radiation patterns on TDOA-based wireless localization of UAVs. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2019)

    Google Scholar 

  24. Bibb, D.A., Yun, Z., Iskander, M.F.: Machine learning for source localization in urban environments. In: MILCOM 2016 - IEEE Military Communications (2016)

    Google Scholar 

  25. Decawave, APS006 Part 2 Application Note, Non Line of Sight operation and optimization to improve performance in DW1000 Based systems, version 1.5 (2014)

    Google Scholar 

  26. Decawave, APH001 Application Note, DW1000 hardware design guide, version 1.1 (2018)

    Google Scholar 

  27. Saho, K.: Kalman filter for moving object tracking: performance analysis and filter design. Kalman Filters, Theory for Advanced Applications (2017)

    Google Scholar 

  28. Simedroni, X.L.: Indoor positioning using decawave MDEK1001. In: 2020 International Workshop on Antenna Technology (iWAT), Bucharest, Romania (2020)

    Google Scholar 

  29. Delamare, Y., Boutteau, M., Savatier, R., Iriart, N.: Static and dynamic evaluation of an UWB localization system for industrial applications. Science 2(2), 23 (2020)

    Google Scholar 

  30. Wang, J., Wang, M., Yang, D., Liu, F., Wen, Z.: UWB positioning algorithm and accuracy evaluation for different indoor scenes. International Journal of Image and Data Fusion (2021)

    Google Scholar 

  31. MDEK1001 Kit User Manual Module Development & Evaluation Kit for the DWM1001 Version 1.2

    Google Scholar 

  32. IEEE Standard for Local and metropolitan area networks— Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs)

    Google Scholar 

  33. DecaWave, DW1000 User Manual, version 2.11 (2017)

    Google Scholar 

  34. Potortì, F., Sangjoon, F., Ruiz, A.R., Barsocchi, P.: Comparing the performance of indoor localization systems through the EvAAL framework. Sensors 17, 23–27 (2017)

    Google Scholar 

  35. Morawska, B.: Reduction of measurement error in spatial objects’ positioning, BSc Thesis, Faculty of Technical Physics, Information Technology and Applied Mathematics of the Technical University of Lodz (2020)

    Google Scholar 

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Correspondence to Krzysztof Lichy .

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Morawska, B., Lipiński, P., Lichy, K., Koch, P., Leplawy, M. (2021). Static and Dynamic Comparison of Pozyx and DecaWave UWB Indoor Localization Systems with Possible Improvements. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_44

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  • DOI: https://doi.org/10.1007/978-3-030-77970-2_44

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  • Online ISBN: 978-3-030-77970-2

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