ENHANCED ACCURACY IN PENETRATING POSITIONING  USING UWB TECHNOLOGY BASED ON RECEIVED SIGNAL STRENGTH AND MACHINE LEARNING

Nguyễn Thị Huyền; Dương Đức Hà; Trương Anh Dũng; Phạm Thanh Hiệp; Lều Mạnh Cường

doi:10.34238/tnu-jst.12626

ENHANCED ACCURACY IN PENETRATING POSITIONING USING UWB TECHNOLOGY BASED ON RECEIVED SIGNAL STRENGTH AND MACHINE LEARNING

About this article

Received: 18/04/25 Revised: 28/05/25 Published: 28/05/25

Authors

1. Nguyen Thi Huyen, Le Quy Don Technical University
2. Duong Duc Ha , Le Quy Don Technical University
3. Truong Anh Dung, Le Quy Don Technical University
4. Pham Thanh Hiep, Le Quy Don Technical University
5. Leu Manh Cuong, Le Quy Don Technical University

Abstract

This paper proposes a novel method to enhance accuracy in ultra-wideband penetrating positioning systems by using the raw data elimination technique combined with a machine learning model applied to received signal strength data. The emergence of ultra-wideband technology has addressed many challenges related to radio frequency spectrum scarcity, offering high precision in distance measurement and positioning. However, it still faces significant challenges such as multipath propagation, scattering, and refraction which degrade system performance. To address these issues, various signal processing approaches have been utilized, including machine learning techniques. In the proposed approach, an optimized LightGBM-based machine learning model is employed, which significantly improves the accuracy of ultra-wideband penetrating positioning systems. Computational results indicate that the proposed method reduces the mean absolute error by 28.2% to 72% compared to existing methods. This represents an effective research direction that addresses complex challenges in the field of radio-based localization and enhances the performance of both penetrating and indoor positioning systems.

Keywords

UWB technology; Received signal strength; Positioning techniques; Machine learning; LightGBM model

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References

[1] P. Groves, Principles of GNSS, Inertial, and Multisensor Integrated Navigation System, 2nd ed., Artech House: Boston, MA, USA, 2013.

[2] O. Sytnik, S. Masalov, P. Kholod, G. Pochanin, and V. Ruban, “UWB Technology for Detecting Alive People Behind Optically Opaque Obstacles,” in Proceedings of the 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS-2018), Odessa, Ukraine, pp. 110-114, 2018.

[3] B. K. Horn, “Doubling the Accuracy of Indoor Positioning: Frequency Diversity,” Sensors, vol. 20, no. 5, 2020, Art. no. 1489.

[4] G. Kia, L. Ruotsalainen, and J. Talvitie, "Toward accurate indoor positioning: An RSS-based fusion of UWB and machine-learning-enhanced WiFi," Sensors, vol. 22, no. 9, 2022, Art. no. 3204.

[5] N. Dvorecki, O. Bar-Shalom, L. Banin, and Y. Amizur, “A machine learning approach for Wi-Fi RTT ranging,” in Proceedings of the 2019 International Technical Meeting of The Institute of Navigation, USA, , pp. 435-444, 2019.

[6] S. Ouadfeul and L. Aliouane, “Multiscale analysis of 3D GPR data using the continuous wavelet transform,” in Proceedings of the XIII International Conference on Ground Penetrating Radar, Lecce, Italy, June 2010, pp. 1-4.

[7] P. E. Hart, “How the Hough transform was invented,” IEEE Signal Process. Mag., vol. 26, pp. 18–22, 2009.

[8] O. Dumin, V. Plakhtii, O. Pryshchenko, and G. Pochanin, “Comparison of ANN and Cross-Correlation Approaches for Ultra Short Pulse Subsurface Survey,” in Proceedings of the 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, February 2020, pp. 381–386.

[9] T. H. Nguyen, D. H. Duong, and T. H. Pham, “Buried objects detection in heterogeneous environment using UWB systems combined with curve fitting method,” ICT Express, vol. 6, no. 4, pp. 348-352, 2020.

[10] R. M. M. R. Rathnayake et al., "RSSI and machine learning-based indoor localization systems for smart cities," Eng., vol. 4, no. 2, pp. 1468-1494, 2023.

[11] S. Sadowski and P. Spachos, “RSSI-Based Indoor Localization with the Internet of Things,” IEEE Access, vol. 6, pp. 30149–30161, 2018.

[12] T. Yang, A. Cabani, and H. Chafouk, “A Survey of Recent Indoor Localization Scenarios and Methodologies,” Sensors, vol. 21, 2021, Art. no. 8086.

[13] Z. D. Tekler, R. Low, C. Yuen, L. Blessing, “Plug-Mate: An IoT-based occupancy-driven plug load management system in smartbuildings,” Build. Environ., vol. 223, 2022, Art. no. 109472.

[14] P. Bahl and V. N. Padmanabhan, ‘‘RADAR: An in-building RF-based user location and tracking system,’’ in Proc. IEEE INFOCOM Conf. Comput. Commun. 19th Annu. Joint Conf. IEEE Comput. Commun. Societies, vol. 2, Mar. 2000, pp. 775–784.

[15] J. V. García, “Characterization of the log-normal model for received signal strength measurements in real wireless sensor networks,” Sensor Actuator Netw., vol. 9, no. 1, Feb. 2020, Art. no. 12.

[16] FCC, “Report and order 02-48,” 2002.

[17] X. Chen, and S. Kiaei, “Monocycle shapes for ultra wideband system,” Proceedings of 2002 IEEE International Symposium on Circuits and Systems, IEEE, 2002, vol. 1, pp. I597 – I600.

[18] L. Qiao, Y. Qin, X. Ren, and Q. Wang,“Identification of buried objects in GPR using amplitude modulated signals extracted from multiresolution monogenic signal analysis,” Multidisciplinary Digital Publishing Institute, Sensors, vol. 15, no. 12, pp. 30340–30350, 2015.

[19] J. Li, T. Guo, H. Leung, H. Xu, L. Liu, B. Wang, and Y. Liu, “Locating Underground pipe using wideband chaotic ground penetrating radar,” Sensors, vol. 19, no. 13, 2019, Art. no. 2913.

[20] H. Duong et al., “Locating the buried object using uwb system with hilbert transform and the least square curve fitting algorithm,” TNU Journal of Science and Technology, vol. 228, no. 6, pp. 77-84, 2023.

DOI: https://doi.org/10.34238/tnu-jst.12626

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