An Online Correction Method for System Errors in the Pipe Jacking Inertial Guidance System

Yutong Zu, Lu Wang (), Zheng Zhou, Da Gong, Yuanbiao Hu and Gansheng Yang
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Yutong Zu: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Lu Wang: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Zheng Zhou: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Da Gong: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Yuanbiao Hu: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Gansheng Yang: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences, Beijing 100083, China

Mathematics, 2025, vol. 13, issue 17, 1-20

Abstract: The pipe-jacking inertial guidance method is a key technology to solve the guidance problems of complex pipe-jacking projects, such as long distances and curves. However, since its guidance information is obtained by gyroscope integration. System errors will accumulate over time and affect the guidance accuracy. To address the above issues, this study proposes an intelligent online system error correction scheme based on single-axis rotation and data backtracking. The method enhances system observability by actively exciting the sensor states and introducing data reuse technology. Then, a Bayesian optimization algorithm is incorporated to construct a multi-objective function. The algorithm autonomously searches for the optimal values of three key control parameters, thereby constructing an optimal correction strategy. The results show that the inclination accuracy improving by 99.36%. The tool face accuracy improving by 94.05%. The azimuth accuracy improving by 94.42% improvement. By comparing different correction schemes, the proposed method shows better performance in estimating gyro bias. In summary, the proposed method uses single-axis rotation and data backtracking, and can correct system errors in inertial navigation effectively. It has better value for engineering and provides a technical foundation for high-accuracy navigation in tunnel, pipe-jacking, and other complex tasks with low-cost inertial systems.

Keywords: pipe-jacking inertial navigation system; data backtracking; single-axis rotation; Bayesian optimization algorithm; observability analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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