"Where Am I": Characterization of Ultra-Wide Band Real Time Location System in Line of Sight

Mahasan Mat Ali; Syamimi Shamsuddin; Mohd Nazrin Muhammad; Sayed Kushairi Sayed Nordin; Shariman Abdullah

doi:10.56741/IISTR.esl.00893

Authors

Mahasan Mat Ali
mahasan@utem.edu.my
Universiti Teknikal Malaysia Melaka https://orcid.org/0000-0002-5140-7367
Syamimi Shamsuddin Universiti Sains Malaysia https://orcid.org/0000-0001-6681-0207
Mohd Nazrin Muhammad Universiti Teknikal Malaysia Melaka https://orcid.org/0000-0002-5980-712X
Sayed Kushairi Sayed Nordin Universiti Teknikal Malaysia Melaka https://orcid.org/0000-0002-8962-9678
Shariman Abdullah Universiti Teknikal Malaysia Melaka https://orcid.org/0000-0003-3690-5840

Keywords:

real time location system, ultra-wide band, range error, characterization

Abstract

Absolute position measurement is a necessity in an Autonomous Indoor Mobile Robot (AiMR) built as to compensate the systematic and non-systematic errors caused by the odometry. This paper is set out to characterize the range bias of DecaWave Trek 1000 in Line of Sight (LOS). DecaWave Trek 1000 is a commercially available Real Time Location System (RTLS) based on Ultra-Wide Band (UWB) technology. The main advantage of using such a system is it provide quick solution with best-in-class accuracy of up to +10 cm. The system also compliant with IEEE802.15.4-2011 standards and implemented based on CMOS technology. To validate its performance, four distinct environment scenarios were selected based on their spatial occupancies to represent both favourable and non-favourable operating conditions. A total of 120,000 positioning data were measured and recorded throughout this study. Firstly, the collected data were compared to the ground truth and subjected to statistical analysis to determine whether environment factors significantly influenced ranging errors. Then the range error the results confirm that while the system performs within its specified accuracy under favourable conditions, the non-favourable condition on the other hand shows a drop of 20% in ranging accuracy. Throughout this extensive characterization, a systematic error model and noise estimation were formulated, providing a critical foundation for integrating UWB RTLS measurements into advanced probabilistic localization framework. Looking forward, the developed characterization function, offers strong potential for fusion with filtering algorithm such as Bayesian filters to enhance indoor navigation reliability for an AiMR application.

Downloads

Download data is not yet available.

Author Biographies

Mahasan Mat Ali, Universiti Teknikal Malaysia Melaka

is a senior academic at the Faculty of Industrial & Manufacturing Technology & Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia. His research interests focus on engineering design, manufacturing systems, and technological innovation in industrial applications. He is actively involved in collaborative projects aimed at enhancing production efficiency and workforce readiness in Industry 4.0 contexts. (email: mahasan@utem.edu.my).

Syamimi Shamsuddin, Universiti Sains Malaysia

is a researcher at the Advanced Medical and Dental Institute, Universiti Sains Malaysia (USM), Pulau Pinang, Malaysia. Her interdisciplinary research encompasses biomedical engineering, health technology integration, and human-centered design. She is passionate about applying engineering principles to improve healthcare systems and educational models in medical science. (email: syamimi.s@usm.edu.my).

Mohd Nazrin Muhammad, Universiti Teknikal Malaysia Melaka

is a faculty member at the Faculty of Industrial & Manufacturing Technology & Engineering, Universiti Teknikal Malaysia Melaka, and serves as the corresponding author for this article. His expertise lies in smart manufacturing, digital transformation, and human–machine collaboration in industrial settings. He has published extensively in national and international journals on the integration of ICT in industrial education and production environments. (email: nazrin@utem.edu.my).

Sayed Kushairi Sayed Nordin, Universiti Teknikal Malaysia Melaka

is affiliated with the Faculty of Industrial & Manufacturing Technology & Engineering, Universiti Teknikal Malaysia Melaka (UteM). His academic and research work centers on robotics, automation, and advanced manufacturing processes. He is actively engaged in promoting STEM education and the digitalization of learning in technical and vocational training institutions. (email: sayedkushairi@utem.edu.my).

Shariman Abdullah, Universiti Teknikal Malaysia Melaka

is a lecturer at the Faculty of Industrial & Manufacturing Technology & Engineering, Universiti Teknikal Malaysia Melaka (UteM). His research focuses on materials engineering, engineering education, and sustainable manufacturing practices. He has contributed to curriculum development initiatives that aim to align industrial training with contemporary technological advancements. (email: shariman@utem.edu.my).

References

J. Palacín, E. Rubies, and E. Clotet, “Systematic odometry error evaluation and correction in a Human-Sized Three-Wheeled omnidirectional mobile robot using Flower-Shaped calibration trajectories,” Applied Sciences, vol. 12, no. 5, p. 2606, Mar. 2022. DOI: https://doi.org/10.3390/app12052606

M. B. Alatise and G. P. Hancke, “A review on challenges of autonomous mobile robot and sensor fusion methods,” IEEE Access, vol. 8, pp. 39830–39846, Jan. 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2975643

Md. A. K. Niloy et al., “Critical design and control issues of indoor Autonomous mobile robots: a review,” IEEE Access, vol. 9, pp. 35338-35370, Jan. 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3062557

I. Oppermann, M. Hämäläinen, J, Iinatti, UWB: theory and applications, John Wiley & Sons; 2004 Oct 15. DOI: https://doi.org/10.1002/0470869194

P. S. Farahsari, A. Farahzadi, J. Rezazadeh, and A. Bagheri, “A survey on indoor positioning systems for IoT-Based applications,” IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7680–7699, Feb. 2022. DOI: https://doi.org/10.1109/JIOT.2022.3149048

N. S. Ahmad, “Recent Advances in WSN-based Indoor Localization: A Systematic review of emerging technologies, methods, challenges and trends,” IEEE Access, vol. 12, pp. 180674–180714, Jan. 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3509516

H. Obeidat, W. Shuaieb, O. Obeidat, and R. Abd-Alhameed, “A review of indoor localization techniques and wireless technologies,” Wireless Personal Communications, vol. 119, no. 1, pp. 289–327, Feb. 2021. DOI: https://doi.org/10.1007/s11277-021-08209-5

Zhang, Shuning, Jingyu Yang, Lichao Zhang, and Jianxin Ai. “A research on UWB precise positioning algorithm using TOA algorithm of two-way ranging.” Journal of Aeronautics, Astronautics and Aviation, vol. 54, no. 4, pp. 375-391, Dec. 2022.

K. Paszek, D. Grzechca, and A. Becker, “Design of the UWB Positioning System Simulator for LOS/NLOS environments,” Sensors, vol. 21, no. 14, p. 4757, Jul. 2021. DOI: https://doi.org/10.3390/s21144757

F. Elbahhar and A. Rivenq, New approach of indoor and outdoor localization systems. 2012. DOI: https://doi.org/10.5772/2751

J. Fontaine, M. Ridolfi, B. Van Herbruggen, A. Shahid, and E. De Poorter, “Edge inference for UWB ranging error correction using autoencoders,” IEEE Access, vol. 8, pp. 139143–139155, Jan. 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3012822

W. Zhao, J. Panerati, and A. P. Schoellig, “Learning-Based bias correction for time difference of arrival Ultra-Wideband localization of Resource-Constrained mobile robots,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3639–3646, Mar. 2021. DOI: https://doi.org/10.1109/LRA.2021.3064199

M. A. Shalaby, C. C. Cossette, J. R. Forbes, and J. L. Ny, “Calibration and Uncertainty Characterization for Ultra-Wideband Two-Way-Ranging Measurements,” IEEE International Conference on Robotics and Automation (ICRA), vol. 20, pp. 4128–4134, May 2023. DOI: https://doi.org/10.1109/ICRA48891.2023.10160769

L. Flueratoru, S. Wehrli, M. Magno, E. S. Lohan, and D. Niculescu, “High-Accuracy ranging and localization with ultrawideband communications for Energy-Constrained devices,” IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7463–7480, Nov. 2021. DOI: https://doi.org/10.1109/JIOT.2021.3125256

Decawave, “TREK1000 User Manual,” 2016.