A Double-Weighted Information Fusion Method Based on Deep Network for Attitude Measurement

The shield machine is a critical piece of equipment for tunnel construction and necessitates high-precision attitude measurement for ensuring construction quality. During tunneling through hard rocks, the machine produces intense vibrations, which detrimentally affect the precision of attitude measu...

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Bibliographic Details
Published in:IEEE sensors journal 2024-04, Vol.24 (7), p.9555-9561
Main Authors: Zhang, Wanpeng, Kong, Kang, Zhu, Chenxi, Zhang, Peng, Zhang, Dailin
Format: Article
Language:English
Subjects:
Algorithms
Angular velocity
Attitudes
Complementary filter (CF)
Data integration
Error analysis
Fiber optic gyroscopes
Frequency measurement
Gyroscopes
Inclinometers
information fusion
Kalman filter (KF)
Kalman filters
Measuring instruments
Noise measurement
Phase lag
Sensors
Tunnel construction
Velocity measurement
Vibration measurement
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Summary:The shield machine is a critical piece of equipment for tunnel construction and necessitates high-precision attitude measurement for ensuring construction quality. During tunneling through hard rocks, the machine produces intense vibrations, which detrimentally affect the precision of attitude measurement instruments, subsequently impacting the accuracy of the machine's attitude assessment. In this article, we present a novel approach that introduces a double-weighted information fusion method to coordinate the capabilities of fiber optic gyroscopes and inclinometers. This method dynamically adjusts two parameters, allocating distinct weights to the posterior estimates of the Kalman filter (KF) and the original angular velocity values. In addition, it integrates a complementary filter (CF) with a KF, aiming to counteract the phase lag introduced by the KF during periods of attitude change. Extensive comparison experiments validate the algorithm's effectiveness. Experimental results demonstrate that the proposed double-weighted fusion (DWF) algorithm decreases the average angular measurement error by 50% relative to the KF and by 46.7% compared to the adaptive KF. The proposed method enhances the system's capability to measure attitude angles. It is well suited for high-precision attitude measurement of engineering equipment operating under conditions of strong vibrations.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3362823