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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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
Mautz, R.: Indoor positioning technologies, Habilitation Thesis, Institute of Geodesy and Photogrammetry, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich (2012)
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)
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)
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)
Barbour, N.M., Stark Draper, C.: Inertial Navigation Sensors, Laboratory (P-4994), Cambridge, MA 02139, USA (2011)
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)
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)
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)
Zimmerman, T., Zimmermann, A.: Magic Quadrant for Indoor Location Services, Global Published 13 January 2020 - ID G00385050 (2020)
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)
Decawave, APS011 Application Note, Sources of Error in DW1000 Based Two-Way Ranging (TWR) Schemes (2014)
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)
Pozyx Homepage. https://www.pozyx.io. Accessed 01 Feb 2021
Decawave DW1000 product homepage. https://www.decawave.com/product/dw1000-radio-ic/. Accessed 01 Feb 2021
Zebra Homepage. https://www.zebra.com/us/en/products/location-technologies/ultra-wideband.html. Accessed 01 Feb 2021
Ubisense Home Site. https://ubisense.com/dimension4/. Accessed 01 Feb 2021
BeeSpoon Mek 1 Product Homepage. https://bespoon.xyz/produit/mek1-ultra-wideband-module-evaluation-kit/. Accessed 01 Feb 2021
NXP Homepage. https://www.nxp.com/applications/enabling-technologies/connectivity/ultra-widebanduwb:UWB. Accessed 01 Feb 2021
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
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)
Daszuta, M., Szajerman, D., Napieralski, P.: New emotional model environment for navigation in a virtual reality. Open Phys. 18(1), 864–870 (2020)
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)
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)
Bibb, D.A., Yun, Z., Iskander, M.F.: Machine learning for source localization in urban environments. In: MILCOM 2016 - IEEE Military Communications (2016)
Decawave, APS006 Part 2 Application Note, Non Line of Sight operation and optimization to improve performance in DW1000 Based systems, version 1.5 (2014)
Decawave, APH001 Application Note, DW1000 hardware design guide, version 1.1 (2018)
Saho, K.: Kalman filter for moving object tracking: performance analysis and filter design. Kalman Filters, Theory for Advanced Applications (2017)
Simedroni, X.L.: Indoor positioning using decawave MDEK1001. In: 2020 International Workshop on Antenna Technology (iWAT), Bucharest, Romania (2020)
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)
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)
MDEK1001 Kit User Manual Module Development & Evaluation Kit for the DWM1001 Version 1.2
IEEE Standard for Local and metropolitan area networks— Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs)
DecaWave, DW1000 User Manual, version 2.11 (2017)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-77970-2_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77969-6
Online ISBN: 978-3-030-77970-2
eBook Packages: Computer ScienceComputer Science (R0)