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Yao L S, Zhou Y L, Zhao S, et al. A FOG start-up drift compensation method at full temperatures before and after compensation comparison[J]. Opto-Electron Eng, 2024, 51(5): 240033. doi: 10.12086/oee.2024.240033
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A FOG start-up drift compensation method at full temperatures before and after compensation comparison
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Abstract
This paper proposes a novel method of compensating for the fiber optic gyroscope (FOG) temperature drift at full temperatures: the temperature field inside the NFS is constructed by multiple temperature variables, which are composed of the thermometer information built in the three inertial sensors, and then the support vector regression (SVR) is used to describe the relationship between the multiple temperature variables and the temperature drift error of the FOG, and finally the sparrow search algorithm (SSA) is applied to tune the model parameters to improve the accuracy and generalization capability. The experimental results validate the effectiveness of the proposed method, and we improve the accuracy of the NFS start-up stage from 0.0209° to 0.0101°. The performance is closely comparable to that of the stable stage, and improves the fast response capability of NFS at different initial temperatures. -
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References
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Overview
In the areas of geodesy, mining, and missiles, it is critical to obtain information about the orientation of an object with respect to the geographic coordinate system. A north finding system (NFS) is an instrument that can provide real north orientation, and accuracy and alignment time are two essential parameters of NFS. Shortening the alignment time of NFS can improve the starting speed of weapons and machines. With the advantages of high reliability, low cost, and less environmental requirements, NFS with fiber optic gyroscope (FOG) has become an active trend in inertial technology research. Fiber-NFS consists of a fiber optic gyroscope and two quartz flexible accelerometers (QFAs). However, as the core component of NFS, FOG is susceptible in temperature changes, especially during the start-up stage, the internal units of FOG generate a lot of heat leading to drastic changes in the thermal environment, which will cause the drift error in the output of FOG, and this non-zero mean drift error will greatly affect the accuracy of the system. Traditional compensation methods usually focus on modeling the stable working stage of the FOG, which has limited effectiveness in compensating for the temperature drift during the start-up stage.
In order to satisfy the requirements of high accuracy and fast response of NFS at different initial temperatures, a novel temperature drift compensation method is proposed in this paper: We combined the information from the built-in thermometers of the three inertial sensors, using their temperature, rate of change of temperature and temperature gradient as input, which provides a more comprehensive description of the complex temperature field inside the NFS. The wavelet transformation (WT) is used to eliminate the non-temperature noise and extract the temperature drift signal accurately, then SVR is used to describe the relationship between multiple temperature variables and drift errors, and finally the accuracy and generalization ability of the model is improved by using the sparrow search algorithm (SSA). The experimental results validate the effectiveness of the proposed method, and we improve the accuracy of the NFS start-up stage from 0.0209° to 0.0101°. The performance is closely comparable to that of the stable stage.
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Yao L S, Zhou Y L, Zhao S, et al. A FOG start-up drift compensation method at full temperatures before and after compensation comparison[J]. Opto-Electron Eng, 2024, 51(5): 240033. doi: 10.12086/oee.2024.240033
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Figure 1.
FOG preprocessing outputs at different initial temperatures
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Figure 2.
Start-up characteristics of the FOG at different initial temperatures
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Figure 3.
FOG preprocessing output and NFS internal temperature information. (a) The original output of the FOG and the denoised signal after preprocessing with WT; (b) NFS internal temperature; (c)Temperature ramp; (d)Temperature gradient
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Figure 4.
The sparrow population position update
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Figure 5.
SSA tuning SVR algorithm flow chart
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Figure 6.
Experimental equipment
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Figure 7.
Calibration experimental progress
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Figure 8.
Before and after compensation comparison
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