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

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 degrees/s, which improves the measurement performance of the fiber optic gyroscope.