Multi-frequency and multi-system GNSS positioning data fusion algorithm based on Kalman filter
Abstract
With the widespread application of Global Navigation Satellite System (GNSS) in the fields of positioning and navigation, traditional single frequency and single system positioning methods are gradually unable to meet the requirements of high accuracy and high reliability. Especially in complex and dynamic environments, GNSS signals are affected by multipath effects, occlusion, and interference, resulting in a significant decrease in positioning accuracy. Therefore, it is particularly important to develop a multi-frequency and multi-system GNSS positioning data fusion algorithm. This article used Kalman filtering technology and combined the data characteristics of multi-frequency and multi-system GNSS signals to study a new positioning data fusion algorithm. By comprehensively processing different GNSS systems and frequency signals, the positioning accuracy and anti-interference ability were significantly improved. The experimental results showed that the algorithm studied improved the average positioning accuracy by more than 6.23% in complex environments compared to traditional methods, and also exhibited good adaptability and stability under dynamic conditions. Fully utilizing the advantages of multi-frequency signals and combining advanced data fusion technology is an effective way to improve GNSS positioning performance, providing new ideas and methods for future intelligent navigation applications.
References
1. Cheng L, Yang J, Gao T. Research on the fusion correction algorithm of speed measurement and positioning data of pipeline logistics system. Jiangxi Metallurgical, 2023, 43(4): 342-351
2. Bai Y, Lian B, Liu Y. Radio data fusion positioning method for aviation search and rescue. Radio Communication Technology, 2024, 50(1): 25-31
3. Zhao Q, Zhang S. Improved single-star direct positioning method for real-valued space-time subspace data fusion. Signal Processing, 2024, 40(6): 1111-1121
4. Acar U. IMU and Bluetooth Data Fusion to Achieve Submeter Position Accuracy in Indoor Positioning. Photogrammetric Engineering & Remote Sensing, 2023, 89(12): 735-740.
5. Ghazal T M. Data Fusion-based machine learning architecture for intrusion detection. Computers, Materials & Continua, 2022, 70(2): 3399-3413.
6. Xu C, Chen G, Hu N. Beidou/GPS dual-mode data fusion trajectory positioning based on Kalman filtering. Metrology and Testing Technology, 2024, 51(4): 10-13
7. Wang P, Wang D, He J. Research on the visual inertial adaptive fusion positioning method of Kalman filtering in error state. Aviation Science and Technology, 2024, 35(4): 104-111
8. Bakhshi Ostadkalayeh F, Moradi S, Asadi A, et al. Performance improvement of LSTM-based deep learning model for streamflow forecasting using Kalman filtering. Water Resources Management, 2023, 37(8): 3111-3127.
9. Ghansah B, Benuwa B B, Essel D D, et al. A Review of Non-Linear Kalman Filtering for Target Tracking. International Journal of Data Analytics (IJDA), 2022, 3(1): 1-25.
10. Winiwarter L, Anders K, Czerwonka-Schröder D, et al. Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering. Earth Surface Dynamics, 2023, 11(4): 593-613.
11. Pizarro G, Poblete P, Droguett G, et al. Extended kalman filtering for full-state estimation and sensor reduction in modular multilevel converters. IEEE Transactions on Industrial Electronics, 2022, 70(2): 1927-1938.
12. Pereira R F R, Albuquerque F P, Liboni L H B, et al. Estimation of the electrical parameters of overhead transmission lines using Kalman Filtering with particle swarm optimization. IET Generation, Transmission & Distribution, 2023, 17(1): 27-38.
13. Hwang J S, Kwon D K, Kareem A. A modal‐based Kalman filtering framework for mode extraction and decomposition of damped structures. Computer‐Aided Civil and Infrastructure Engineering, 2023, 38(10): 1274-1289.
14. Avrutov V, Bouraou N, Davydenko S, et al. Wavelet de-noising and Kalman filtering of MEMS sensors for autonomous latitude determination. International Journal of Sensors Wireless Communications and Control, 2022, 12(5): 344-351.
15. Menner M, Berntorp K, Di Cairano S. Automated controller calibration by Kalman filtering. IEEE Transactions on Control Systems Technology, 2023, 31(6): 2350-2364.
16. Ghorbani E, Afshari S S, Svecova D, et al. Time‐varying reliability analysis based on hybrid Kalman filtering and probability density evolution. Earthquake Engineering & Structural Dynamics, 2024, 53(3): 1326-1344.
Copyright (c) 2025 Zerui Chen, Yanhong Xiao, Jiaxiang Ou, Xin Wu, Houpeng Hu, Jian Xiao, Shang Yang, Zhenghao Gao
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
