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Collaborative navigation method based on adaptive time-varying factor graph

Published online by Cambridge University Press:  17 January 2025

H. Wang
Affiliation:
School of Information and Communication Engineering, Harbin Engineering University, Harbin, China
L. Hu*
Affiliation:
School of Information and Communication Engineering, Harbin Engineering University, Harbin, China
J. Tao
Affiliation:
School of Information and Communication Engineering, Harbin Engineering University, Harbin, China
*
Corresponding author: L. Hu; Email: [email protected]

Abstract

Aiming at the problems of poor coordination effect and low positioning accuracy of unmanned aerial vehicle (UAV) formation cooperative navigation in complex environments, an adaptive time-varying factor graph framework UAV formation cooperative navigation algorithm is proposed. The proposed algorithm uses the factor graph to describe the relationship between the navigation state of the UAV fleet and its own measurement information as well as the relative navigation information, and detects the relative navigation information at each moment by the double-threshold detection method to update the factor graph model at the current moment. And the robust estimation is combined with the factor graph, and the weight function measurements are used in the construction of the factor nodes for adaptive adjustment to make the system highly robust. The simulation results show that the proposed method realises the effective fusion of airborne multi-source sensing information and relative navigation information, which effectively improves the UAV formation cooperative navigation accuracy.

Type
Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Zhou, X., Tang, Z., Wang, N.,Yang, C. and Huang, T. A novel state transition algorithm with adaptive fuzzy penalty for multi-constraint UAV path planning, Expert Syst. Appl., 2024, 248, p 123481.CrossRefGoogle Scholar
Upadhyay, J., Rawat, A. and Deb, D. Multiple drone navigation and formation using selective target tracking-based computer vision, Electronics, 2021, 10, (17), p 2125.CrossRefGoogle Scholar
Wang, S., Zhan, X., Zhai, Y., Shen, J. and Wang, H. Performance estimation for kalman filter based multi-agent cooperative navigation by employing graph theory, Aerospace Sci. Technol., 2021, 112, p 106628.CrossRefGoogle Scholar
Xu, L., Liu, J., Xie, L. and He, X. Multi-uav navigation and recharging for fair and sustainable coverage in wireless networks, Proceedings of the 3rd International Conference on Advanced Information Science and System, 2021, pp 16.CrossRefGoogle Scholar
Hu, G., Gao, B., Zhong, Y. and Gu, C. Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system, Inf. Fusion, 2020, 64, pp 194204.CrossRefGoogle Scholar
Houzeng, H., Jian, W. and Mingyi, D. Gps/bds/ins tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update, Chin. J. Aeronaut., 2018, 31, (3), pp 556566.Google Scholar
Bodi, M., Zhenbao, L., Jiang, F., Wen, Z., Qingqing, D., Xiao, W., Zhang, J. and Lina, W. Reinforcement learning based uav formation control in gps-denied environment, Chin. J. Aeronaut., 2023, 36, (11), pp 281296.Google Scholar
Bai, M., Huang, Y., Zhang, Y. and Chen, F. A novel heavy-tailed mixture distribu tion based robust kalman filter for cooperative localization, IEEE Trans. Ind. Inf., 2020, 17, (5), pp 36713681.CrossRefGoogle Scholar
Wang, S., Zhan, X., Zhai, Y., Shen, J. and Wang, H. Performance estimation for kalman filter based multi-agent cooperative navigation by employing graph theory, Aerospace Sci. Technol., 2021, 112, p 106628.CrossRefGoogle Scholar
Xiao, X., Shi, C., Yang, Y., Liang, Y. and Guo, X. An adaptive ins/gps/vps federal Kalman filter for UAV based on SVM, 2017 13th IEEE Conference on Automation Science and Engineering (CASE), 2017, pp 16511656.CrossRefGoogle Scholar
Ben, Y., Sun, Y., Li, Q. and Zang, X. A novel cooperative navigation algorithm based on factor graph with cycles for auvs, Ocean Eng., 2021, 241, p 110024.CrossRefGoogle Scholar
Ma, X., Liu, X., Li, C.L. and Che, S. Multi-source information fusion based on factor graph in autonomous underwater vehicles navigation systems, Assem. Autom., 2021, 41, (5), pp 536545.CrossRefGoogle Scholar
Huang, Z., Chai, H., Xiang, M., Li, D., Du, Z. and Wang, D. Multi-source information fusion localization algorithm based on auv factor graph considering information delay, J. Chin. Inertial Technol., 2021, 29, (5), pp 625631.Google Scholar
Du, X., Pang, X., Guan, F., Hu, J. and Zhang, W. A novel multi-source navigation algorithm based on factor graph in complex underwater environments of polar regions, Ocean Eng., 2024, 301, p 117516.CrossRefGoogle Scholar
Yan, Z., Luan, Z., Liu, J. and Xing, W. A cooperative localization method for mul- tiple unmanned underwater vehicles based on improved factor graph, 2023 IEEE International Conference on Mechatronics and Automation (ICMA), 2023, pp 894899.CrossRefGoogle Scholar
Chen, M., Xiong, Z., Liu, J., Wang, R. and Xiong, J. Distributed cooperative navigation method for uav swarm based on factor graph, J. Chin. Inertial Technol., 2020, 28, (4), pp 456461.Google Scholar
Elisha, Y.B. and Indelman, V. Active online visual-inertial navigation and sensor calibration via belief space planning and factor graph based incremental smoothing, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp 26162622.CrossRefGoogle Scholar
Dai, H., Bian, H., Ma, H. and Wang, R. Application of robust incremental smoothing algorithm based on factor graph in integrated navigation of unmanned surface vehicle, J. Chin. Inertial Technol., 2018, 26, (06), pp 778786.Google Scholar
Liu, S., Zhang, T., Zhang, J. and Zhu, Y. A new coupled method of sins/dvl integrated navigation based on improved dual adaptive factors, IEEE Trans. Instrum. Meas., 2021, 70, pp 111.Google Scholar
Ouyang, X., Zeng, F., Lv, D., Dong, T. and Wang, H. Cooperative navigation of UAVs in GNSS-denied area with colored RSSI measurements, IEEE Sens. J., 2020, 21, (2), pp 21942210.CrossRefGoogle Scholar