A temperature drift compensation method for fiber optic gyroscope based on wavelet analysis and bp neural network
A BP neural network and fiber optic gyroscope technology, applied in Sagnac effect gyroscopes and other directions, can solve the problems of model accuracy and other problems, and achieve the effects of improving accuracy, reducing computational burden, and shortening startup time.
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Embodiment 1
[0032] like figure 1 Shown, the present invention is a kind of fiber optic gyroscope temperature drift compensation method based on wavelet denoising and BP neural network fitting, and its steps are as follows:
[0033] (1) Fix a certain type of fiber optic gyroscope on a horizontal stationary platform, with the sensitive axis pointing to the sky, collect multiple sets of gyroscope signals and temperature outputs started at room temperature, and use the formula (1) to compensate the earth's rotation angular velocity component for the gyroscope output signal Get the gyro zero drift.
[0034] ε=ω ib -ω ie sinL(1)
[0035] In the formula, ε is the gyro drift item; ω ib is the gyro output; ω ie is the angular velocity of the earth's rotation; L is the local latitude.
[0036] figure 2 It is one of the groups where the gyro drift and temperature are collected. Depend on figure 2 It can be seen that the standard deviation of the gyro drift is 0.110(°) / h, and it reaches 0....
Embodiment 2
[0049] Utilize the present invention to carry out compensation experiments on the gyro drift collected by a certain fiber optic gyro started multiple times at different times, Figure 7 Compensation effects for 4 of these experiments are shown. Table 2 shows the standard deviation before and after the FOG drift compensation. It can be seen that after the temperature drift is compensated by the present invention, the standard deviation of the optical fiber gyro drift is increased by 6 times.
[0050] Table 2 is the experimental verification effect of the present invention ((°) / h)
[0051]
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