Improved Indoor Positioning Model Based on UWB/IMU Tight Combination with Double-Loop Cumulative Error Estimation
Source abstract
Abstract
With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversional UICMs suffer from a high probability of positioning failure due to uncontrollable and cumulative errors from inertial measuring units (IMU). Hence, to address this issue, we improved the extended Kalman filter (EKF) algorithm of an indoor positioning model based on UWB/IMU tight combination with a double-loop error self-correction. Compared with conventional UICMs, this improved model consists of new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error estimation, sequentially. Further, systematic error controllability analysis proved that the proposed model could satisfy the controllability of UWB indoor positioning systems. To validate this improved UICM, inevitable obstacles and atmospheric interferences were regarded as Gaussian white noises to verify its environmental adaptability. Finally, the experimental results showed that this proposed model outperformed the state-of-the-art UWB-based positioning models with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Indoor and Outdoor Localization Technologies · Target Tracking and Data Fusion in Sensor Networks · GNSS positioning and interference
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
Funding
National Natural Science Foundation of China · Project of Tongji University Innovative Design and Intelligent Manufacturing Discipline Group
References (28)
Show source-provided references
1. Filippoupolitis, A., Oliff, W., and Loukas, G. (2016, January 14–16). Bluetooth Low Energy Based Occupancy Detection for Emergency Management. Proceedings of the International Conference on Ubiquitous Computing and Communications, Granada, Spain.10.1109/IUCC-CSS.2016.013 ↗
2. Plug-Mate: An IoT-based occupancy-driven plug load management system in smart buildings10.1016/j.buildenv.2022.109472 ↗
3. A Scalable Bluetooth Low Energy Approach to Identify Occupancy Patterns and Profiles in Office Spaces10.1016/j.buildenv.2020.106681 ↗
4. Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications10.1109/COMST.2016.2632427 ↗
5. Li, H. (2020). Research on Indoor Positioning Method Based on Multi-Sensor Combination. [Ph.D. Thesis, Nanchang University]. Volume 19, pp. 1327–1346. (In Chinese).
6. A Low-Cost INS and UWB Fusion Pedestrian Tracking System10.1109/JSEN.2019.2894714 ↗
7. Liu, F., Li, X., Wang, J., and Zhang, J. (2019). An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment. Remote Sens., 11.10.3390/rs11222628 ↗
8. Yue, X., Jiang, Z., Guo, P., Li, H., Liu, X., Li, M., and Liu, F. (2022, January 4–6). Research on Fusion and Location Method of Indoor Targeted Spraying Robots Based on UWB\IMU. Proceedings of the 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.10.1109/ITOEC53115.2022.9734704 ↗
9. Shi, Y., Zhang, Y., Li, Z., Yuan, S., and Zhu, S. (2023). IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation. Sensors, 23.10.3390/s23156700 ↗
10. Delamare, M., Boutteau, R., Savatier, X., and Iriart, N. (October, January 30). Evaluation of an UWB localization system in static and dynamic. Proceedings of the Internation Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy.
11. Range-Focused Fusion of Camera-IMU-UWB for Accurate and Drift-Reduced Localization10.1109/LRA.2021.3057838 ↗
12. On-field mounting position estimation of a lidar sensor
13. Deng, T., Bazin, J.C., Martin, T., Kuster, C., Cai, J., Popa, T., and Gross, M. (2014, January 14–18). Registration of multiple RGBD cameras via local rigid transformation. Proceedings of the International Conference on Multimedia and Expo (ICME), Chengdu, China.10.1109/ICME.2014.6890122 ↗
14. Using Adaptive Motion Constraints to Support UWB/IMU Based Navigation10.1002/navi.227 ↗
15. Basar, T. (2001). Control Theory: Twenty-Five Seminal Papers (1932–1981), Wiley-IEEE Press.
16. Li, Q., Li, R., Ji, K., and Dai, W. (2015, January 1–3). Kalman Filter and Its Application. Proceedings of the 2015 8th International Conference on Intelligent Networks and Intelligent Systems, Tianjin, China.10.1109/ICINIS.2015.35 ↗
17. Santosh, D.H., and Mohan, P.G.K. (2014, January 8–10). Multiple objects tracking using Extended Kalman Filter, GMM and Mean Shift Algorithm—A comparative study. Proceedings of the 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, Chennai, India.10.1109/ICACCCT.2014.7019350 ↗
18. Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations10.1109/TSP.2010.2056923 ↗
19. Xiong, H., Wang, X., Zhu, J., and Dai, D. (2014, January 19–23). Research on the algorithm of gravity aided Inertial Navigation based on CKF. Proceedings of the International Conference on Signal Processing (ICSP), Hangzhou, China.10.1109/ICOSP.2014.7015007 ↗
20. Dellaertt, F., Fox, D., Burgard, W., and Thrun, S. (1999, January 10–15). Monte Carlo Localization for Mobile Robots. Proceedings of the 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), Detroit, MI, USA.
21. Li, Q., and Yu, S. (2012, January 25–27). Cubature MCL: Mobile robot Monte Carlo Localization based on Cubature Particle Filter. Proceedings of the 31st Chinese Control Conference, Hefei, China.
22. Influence of Atmospheric Medium on the Accuracy of Electromagnetic Ranging
23. Wu, Y. (2010). Design and Implementation of Micro-Inertial Integrated Inertial Navigation System. [Master’s Thesis, Harbin Engineering University].
24. Design of Fall Detection System Based on Inertial Sensor
25. (2020, June 26). RTNS (Real-Time Navigation System). Available online: https://drivextech.github.io/articles/2020-05-31-clock-model-for-uwb-localization/2020-05-31-clock-model-for-uwb-localization.html.
26. (2019, March 23). Invariant Extented Kalman Filter-Lie Group. Available online: http://aipiano.github.io/2019/03/23/%E4%B8%8D%E5%8F%98%E6%89%A9%E5%B1%95%E5%8D%A1%E5%B0%94%E6%9B%BC%E6%BB%A4%E6%B3%A22/#%E8%AF%AF%E5%B7%AE%E4%BC%A0%E9%80%92.
27. Zhao, R.Y. (2020). UWB Positioning Technology and Intelligent Manufacturing Application, Machinery Industry Publishing. [1st ed.].
28. Zeng, Z., Liu, S., and Wang, L. (2019, January 12). UWB NLOS identification with feature combination selection based on genetic algorithm. Proceedings of the IEEE International Conference on Consumer Electronics, Bangkok, Thailand.10.1109/ICCE.2019.8662065 ↗