Fiber optic gyroscope (FOG) is a core component in modern inertial technology. However, the precision and performance of FOG will be degraded by environmental drift, especially in complex temperature environment. As the modeling performance is affected by the noises in the output data of FOG, an improved wavelet threshold value based on Allan variance and Classical variance is proposed for discrete wavelet analysis to decompose the temperature drift trend item and noise items. Firstly, the relationship of Allan variance and Classical variance is introduced by analyzing the drawback of traditional wavelet threshold. Secondly, an improved threshold is put forward based on Allan variance and Classical variance which overcomes the shortcoming of traditional wavelet threshold method. Finally, the innovative threshold algorithm is experimentally evaluated on FOG. The mathematical evaluation results show that the new method can get better signal-to-noise ratio (SNR) and gain the reconstruction signal of the higher correlation coefficient (CC). As an experimental validation, the nonlinear capability of error back propagation neural network (BP neural network) is used to fit the drift trend item and find out the complex relationship between the FOG drift and temperature, and the final processing results indicate that the new denoising method can get better root of mean square error (MSE).
Full Stokes polarimeter for characterization of fiber optic gyroscope coils