LAUSR

Temperature drift is a major error source of the fiber optic gyroscope (FOG), and the method of modeling is important in eliminating it. A method of multiscale modeling based on improved ensemble empirical mode decomposition (EEMD) is proposed in this paper. First, in order to improve the ability of eliminating mode mixing, the influence of the frequency of the masking signal on mode mixing is analyzed. Then we conclude that the frequency of the masking signal should be higher than that of the signal, and an improved EEMD is proposed based on the conclusion. Second, the temperature drift of FOG is filtered by applying the permutation entropy to the intrinsic mode functions (IMFs). Third, the IMFs are divided into several scales according to the mean value and the distribution of instantaneous frequency of IMFs. Finally, the algorithm of the support vector machine is used to model each scale, and the models are accumulated into a total model. The regression error after compensation of the proposed method (in the case of the mean squared error indicator) increased by more than two orders of magnitude compared to the original temperature drift.