基于小波神经网络的光纤陀螺误差补偿方法

骞微著; 杨立保

doi:10.3788/CO.20181106.1024

Volume 11 Issue 6

Dec. 2018

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Article Contents

Chinese Optics > 2018 > 11(6): 1024-1031.

QIAN Wei-zhu, YANG Li-bao. A fiber optic gyro error compensation method based on wavelet neural network[J]. Chinese Optics, 2018, 11(6): 1024-1031. doi: 10.3788/CO.20181106.1024

Citation:

QIAN Wei-zhu, YANG Li-bao. A fiber optic gyro error compensation method based on wavelet neural network[J]. Chinese Optics, 2018, 11(6): 1024-1031. doi: 10.3788/CO.20181106.1024

QIAN Wei-zhu, YANG Li-bao. A fiber optic gyro error compensation method based on wavelet neural network[J]. Chinese Optics, 2018, 11(6): 1024-1031. doi: 10.3788/CO.20181106.1024

Citation:

QIAN Wei-zhu, YANG Li-bao. A fiber optic gyro error compensation method based on wavelet neural network[J]. Chinese Optics, 2018, 11(6): 1024-1031. doi: 10.3788/CO.20181106.1024

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A fiber optic gyro error compensation method based on wavelet neural network

doi: 10.3788/CO.20181106.1024

QIAN Wei-zhu^{1, 2
,},
YANG Li-bao^{1, 3
,
,}

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.
University of Chinese Academy of Sciences, Beijing 100039, China
3.
College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun 130022, China

Funds:

National Natural Science Foundation of China 51275504

More Information

Corresponding author: YANG Li-bao, E-mail:yanglibao228@163.com
Received Date: 11 Nov 2017
Rev Recd Date: 13 Dec 2017
Publish Date: 01 Dec 2018

Abstract

Abstract

In order to improve the measurement accuracy of fiber optic gyroscope, an error compensation method based on wavelet neural network(WNN) is proposed. Firstly, the main trend term in the gyro signal is extracted by the Mallat decomposition algorithm in wavelet analysis, and the error residuals are reconstructed. The reconstructed signal is then used as the target output of the wavelet neural network, and the original gyro signal is used as the training input. In order to improve the training speed of the WNN and prevent it from falling into local minimum values, the method of increasing the momentum factor and adaptively adjusting the learning rate is used. The neural network model established after training has a good ability to estimate the fiber optic gyro error. The final result shows that after the compensation by the WNN method, the output precision of the fiber optic gyroscope reaches 0.0194°/s, which improves the measurement performance of the fiber optic gyroscope.
- fiber optic gyro,
- wavelet neural network,
- wavelet analysis,
- error compensation,
- trend term extraction

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References(15)

References

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