FOG temperature drift compensation method based on wavelet denoising and neural network

Li Guangyao; Hou Honglu; Du Juan; Li Yuan

doi:10.12086/oee.2019.180636

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Li Guangyao, Hou Honglu, Du Juan, et al. FOG temperature drift compensation method based on wavelet denoising and neural network[J]. Opto-Electronic Engineering, 2019, 46(9): 180636. doi: 10.12086/oee.2019.180636

Citation:

Li Guangyao, Hou Honglu, Du Juan, et al. FOG temperature drift compensation method based on wavelet denoising and neural network[J]. Opto-Electronic Engineering, 2019, 46(9): 180636. doi: 10.12086/oee.2019.180636

FOG temperature drift compensation method based on wavelet denoising and neural network

1.
College of Photo-Electric Engineering, Xi'an Technological University, Xi'an, Shaanxi 710000, China
2.
Xi'an Sino Huaxin Measurement & Control CO., LTD, Xi'an, Shaanxi 710000, China

Fund Project: Supported by Shaanxi Province Industry Technology Tackle Project Fund (2016GY-051) and Education Department of Shaanxi Province Key Laboratory science Programs Project Fund (15JS035)

More Information

^*Corresponding author: Hou Honglu, E-mail: hlhou@sina.com

Received Date 03 December 2018

Revised Date 28 February 2019

Published Date 30 September 2019

Abstract

Abstract

The output of fiber optic gyroscope (FOG) is easily affected by the temperature variations, so it leads to produce drift and the measurement accuracy of FOG is reduced. The traditional BP neural network is an optimization method of local search, which is easy to fall into local minimum, leading to the failure of network training. In order to optimize BP neural network, a temperature drift compensation method for FOG based on particle swarm optimization (PSO) and wavelet denoising is proposed. Firstly, the mechanism of FOG temperature drift is analyzed. Next, FOG static state test in different temperatures is finished. Finally, the FOG temperature drift model has been built by the method and compensate. The results show that the output standard deviation of FOG at different temperatures is reduced by 60.19%, and the compensation effect is better than traditional BP neural network.
- fiber optic gyroscope /
- temperature drift compensation /
- Shupe effect /
- wavelet denoising /
- heuristic threshold

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References

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Overview

Overview

Overview: Fiber optic gyroscope (FOG) is a new solid-state optoelectronic gyroscope based on Sagnac effect and it is widely used in servo control, flight control and inertial navigation. The output characteristics of the main components of FOG (such as optical fiber ring, light source, and photoelectric detector, etc.) are vulnerable to the influence of ambient temperature and self-heating. Ultimately, the output of FOG produces temperature drift, which is the comprehensive effect of temperature on the components of FOG. This drift greatly affects the measurement accuracy of FOG, so measures must be taken to suppress the temperature drift of FOG. Restrict by technology and cost, ameliorate the construction of FOG and winding craft of fiber coil can't overcome the influence by temperature completely. Building the temperature drift compensate model can restrain the temperature drift primely and unrestrictedly.

BP neural network is often used in temperature drift modeling of FOG, but the traditional BP neural network is an optimization method of local search. The weights and thresholds of the network are gradually adjusted along the direction of local improvement, which is easy to fall into local minimum, leading to the failure of network training. If the direction of maximum descent gradient is found on a larger scale and the connection weights and thresholds of each layer are adjusted, the local minimum can be avoided to a certain extent, and the fitting effect of the neural network can be improved. At the same time, the noise in the FOG signal will also cause disadvantage to the establishment of temperature drift model, so the signal must be filtered before the model is built.

Guided by the above ideas, a temperature drift compensation method for FOG based on particle swarm optimization BP neural network and wavelet denoising is proposed. Firstly, according to the operating principle of the FOG, the mechanism and the temperature characteristic of FOG temperature drift are analyzed and state the temperature characteristic of the FOG drift. Then, FOG temperature drift static state test within the limits of -40 ℃~60 ℃ is designed and record temperature in real time. The results show that temperature gradient will impact the FOG temperature drift. Next, using heuristic threshold filtering can reduce high frequency noise and eliminate abnormal change data. Using the filtered experimental data, the temperature drift model is established by optimizing BP neural network fitting with particle swarm optimization algorithm. The model can predict the temperature drift in different states. Finally, compensate results are verified by simulation experiments. The results show standard deviation of FOG outputs in different temperatures is descend by 60.19%, and the compensation effect is better than traditional BP neural network.