A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
Source abstract
Abstract
In recent years, with the vigorous construction of 5G networks, the high-density deployment, low delay, and high bandwidth of 5G network systems have enabled high-precision positioning services. By integrating the BeiDou Navigation Satellite System (BDS) and 5G systems, more reliable positioning services can be provided, and BDS–5G signal integrated positioning has become a new research hotspot. However, BDS–5G signal fusion positioning faces the problems of how to build an effective fusion positioning model between heterogeneous systems and the high complexity of multiobjective function positioning solutions. Therefore, this paper constructs a TOA/TDOA fusion positioning model of BDS–5G signals and introduces the multiobjective particle swarm optimization positioning solution method (MOPSO) to realize the reliable positioning of BDS and 5G signal system fusion (MOPSO-TOA/TDOA). The experimental results show that compared with the traditional BDS–5G signal fusion positioning algorithm based on a Kalman filter, the positioning accuracy of the BDS–5G signal joint solution system based on a multiobjective particle fusion algorithm is 24.8% higher than that of the Kalman filter in terms of positioning convergence time, 18.9% higher in terms of system positioning accuracy, and 50–80% higher than that of the BDS positioning system alone, and the reliable positioning ability in complex environments is effectively enhanced.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Indoor and Outdoor Localization Technologies · Inertial Sensor and Navigation · Maritime Navigation and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Beijing Institute of Tracking and Telecommunications Technology, Beijing 100049, China
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
National Time Service Center, Chinese Academy of Sciences, Xi’an 710600, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Funding
National Key R&D Program of China · National Natural Science Foundation of China
References (30)
Show source-provided references
1. Evolution of key technologies and system architecture base on 5G mobile communication system fusion positioning
2. A review of 5G-oriented positioning technology research
3. Tobie, A.M., Garcia-Pena, A., Thevenon, P., Vezinet, J., and Aubault, M. (2020, January 22–25). Hybrid navigation filters performances between GPS, Galileo and 5G TOA measurements in multipath environment. Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS + 2020), St. Louis, MI, USA.10.33012/2020.17521 ↗
4. Multi-Path Assisted Cooperative Radio-Based Localization for Connected Vehicles
5. GPS/Base Station Positioning Information Fusion Algorithm Based on Federated Kalman
6. “BDS + 5G” integrated development opportunities
7. A joint positioning system based on BDS + 5G
8. Research on BDS + 5G Fusion Positioning Technology
9. Jue, Z., ShuGuo, P., and DengHui, W. (2017, January 19–21). Method of the GPS/BDS/GLO/GAL integrated positioning based on DCB correction. Proceedings of the 2017 Forum on Cooperative Positioning and Service (CPGPS), Harbin, China.10.1109/CPGPS.2017.8075114 ↗
10. Bai, L., Sun, C., Zhao, H., Cheong, J.W., Dempster, A.G., and Feng, W. (2021, January 26–28). An EKF-based 5G-GNSS joint synchronization and positioning method. Proceedings of the 12th China Satellite Navigation Annual Conference-S09 User Terminal Technology, Nanchang, China.
11. Destino, G., Saloranta, J., Seco-Granados, G., and Wymeersch, H. (2018, January 28–31). Performance analysis of hybrid 5G-GNSS localization. Proceedings of the 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.10.1109/ACSSC.2018.8645207 ↗
12. Causa, F., Vetrella, A.R., Fasano, G., and Accardo, D. (2018, January 23–26). Multi-UAV formation geometries for cooperative navigation in GNSS-challenging environments. Proceedings of the 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA.10.1109/PLANS.2018.8373453 ↗
13. (2018). 5G System Design: Architectural and Functional Considerations and Long Term Research, John Wiley & Sons.
14. Keating, R., Yoon, D., Tao, T., and Huang, H. (2019, January 22–25). Opportunities and challenges for NR RAT-Dependent based positioning. Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA.10.1109/VTCFall.2019.8891135 ↗
15. Maymo-Camps, R., Vautherin, B., Saloranta, J., and Crapart, R. (2018, January 13–16). 5G positioning and hybridization with GNSS observations. Proceedings of the International Technical Symposium on Navigation and Timing (ITSNT), Toulouse, France.10.31701/itsnt2018.10 ↗
16. GNSS-5G hybrid positioning based on multi-rate measurements fusion and proactive measurement uncertainty prediction
17. Test and Analysis of Precise Time Synchronization Protocol for 5G Network Application
18. Research on high-precision positioning based on 5G network
19. Research progress of communication and navigation integration technology
20. An Improved Chan Algorithm Basing on Particle Filtering in NLOS Environment10.4028/www.scientific.net/AMR.217-218.1564 ↗
21. 3D WLS hybrid and non hybrid localization using TOA, TDOA, azimuth and elevation10.1007/s11235-018-0468-7 ↗
22. Multi-source data fusion for landslide monitoring using mutual information and IPSO-LSTM
23. Xin, Q., and Wang, S. (2020, January 3–5). GPS Positioning Method of UAV Based on Improved Particle Filter. Proceedings of the 2020 International Conference on Communications, Information System and Computer Engineering (CISCE), Kuala Lumpur, Malaysia.10.1109/CISCE50729.2020.00026 ↗
24. Prediction-discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks10.1049/iet-rsn.2019.0520 ↗
25. A Bayesian approach to track multiple extended targets using particle filter for nonlinear system10.1155/2018/7424538 ↗
26. Campos, H.F.S., and Paulino, N. (2021). Multiple target tracking with interaction using an MCMC MRF Particle Filter. arXiv.
27. Application of an improved particle swarm optimization algorithm in multi-objective reactive power optimization
28. Research on TOA/TDOA Fusion Localization Algorithm Based on Particle Filter
29. Deb, K., and Tiwari, S. (2005). Omni-optimizer: A procedure for single and multi-objective optimization. International Conference on Evolutionary Multi-Criterion Optimization, Springer.10.1007/978-3-540-31880-4_4 ↗
30. Su, W., and Deng, Z. (2020). Research on the Weight Distribution of Helmert Variance Component Estimation in Beidou + 5G Integrated Positioning. China Satellite Navigation Conference, Springer.10.1007/978-981-15-3715-8_39 ↗