SINS/DVL integrated navigation method based on adaptive particle swarm optimization in complex underwater environment

被引：0
|
作者：
Wang D. [1 ]
Wang B. [1 ]
Huang H. [1 ]
Wang G. [2 ]
Liu X. [3 ]
Yao Y. [3 ]
机构：
[1] College of Energy and Electrical Engineering, Hohai University, Nanjing
[2] College of Mechanical and Vehicle Engineering, Hunan University, Changsha
[3] School of Instrument Science and Engineering, Southeast University, Nanjing
来源：
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2023年 / 31卷 / 10期
关键词：
integrated navigation; particle swarm optimization; robust filter; strapdown inertial navigation system/Doppler velocity log;
D O I：
10.13695/j.cnki.12-1222/o3.2023.10.010
中图分类号：
学科分类号：
摘要：
To solve the problems that the position accuracy of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integrated navigation system is degraded by interference in complex underwater environment, a SINS/DVL integrated navigation method based on adaptive particle swarm optimization is proposed. Firstly, SINS/DVL navigation model based on the principle of state transformation is constructed to solve the problem of inconsistent variance estimation in the model. Secondly, considering the difficulty of noise estimation based on Kalman filter in complex underwater environment, the fitness function of particle filter is constructed, and an adaptive particle swarm optimization algorithm is designed. Finally, the proposed method is tested by simulation and lake test. The experimental results show that the proposed method can solve the problem of poor positioning accuracy caused by interference of SINS/DVL integrated navigation system in complex underwater environment. Compared with Kalman filtering method and Huber robust filtering method, the maximum horizontal position error of the proposed method is reduced by 49.56% and 41.20%, respectively. © 2023 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.
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页码：1023 / 1029and1036
相关论文
共 19 条
  • [1] Paull L, Saeedi S, Seto M, Et al., AUV navigation and localization: A review, IEEE Journal of Oceanic Engineering, 39, 1, pp. 131-149, (2014)
  • [2] Xu B, Wang L, Li S., Improved Huber robust filter SINS/DVL compact combination fault processing method based on beam reconstruction, Journal of Chinese Inertial Technology, 29, pp. 746-755, (2021)
  • [3] Lu D, Song S, Wang J, Et al., Review on the development of SINS/DVL underwater integrated navigation technology, Control Theory & Applications, 39, pp. 1159-1170, (2022)
  • [4] Song J, Li W, Zhu X, Et al., Underwater adaptive height-constraint algorithm based on SINS/LBL tightly coupled, IEEE Transactions on Instrumentation and Measurement, 71, pp. 1-9, (2022)
  • [5] Jalal F, Nasir F., Underwater navigation, localization and path planning for autonomous vehicles: A review, 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST), pp. 817-828, (2021)
  • [6] Wang D, Xu X, Yao Y, Et al., A novel SINS/DVL tightly integrated navigation method for complex environment, IEEE Transactions on Instrumentation and Measurement, 69, 7, pp. 5183-5196, (2019)
  • [7] Hou L, Xu X, Yao Y, Et al., An M-estimation-based improved interacting multiple model for INS/DVL navigation method, IEEE Sensors Journal, 22, 13, pp. 13375-13386, (2022)
  • [8] Xu X, Gui J, Sun Y, Et al., A robust in-motion alignment method with inertial sensors and Doppler velocity log, IEEE Transactions on Instrumentation and Measurement, 70, pp. 1-13, (2020)
  • [9] Liu S, Zhang T, Zhang J, Et al., A new coupled method of SINS/DVL integrated navigation based on improved dual adaptive factors, IEEE Transactions on Instrumentation and Measurement, 70, pp. 1-11, (2021)
  • [10] Xu B, Guo Y, Hu J., An improved robust Kalman filter for SINS/DVL tightly integrated navigation system, IEEE Transactions on Instrumentation and Measurement, 70, pp. 1-15, (2021)
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