2. Naval 92728, Shanghai 200040, China;
3. Naval 92214, Ningbo, Zhejiang 315000, China
Overview: Fiber optic gyroscope (FOG) is a new inertial sensor based on the Sagnac effect and it is widely used in servo control, flight control and inertial navigation. It has the advantages of high reliability, high measurement accuracy, and ease of integration. It has become an ideal device for inertial navigation systems. The collected fiber optic gyroscope drift data is affected by many factors such as the light source, fiber bending, and ambient temperature, making it often submerged in the noise and leading to difficulties in direct modeling compensation. In order to establish an accurate error compensation model, data preprocessing is demanded to output data on the gyroscope.
In order to reduce the influence of noise on the output signal of fiber optic gyroscope, a de-noising algorithm of fiber optic gyroscope signal based on modified ensemble empirical mode decomposition (MEEMD) and forward linear prediction (FLP) is proposed. At the beginning, we studied the output signal of fiber optic gyroscope in depth and discovered that it is complicated that we cannot reduce the noise directly. As a result, the concept of permutation entropy (PE) is introduced. PE is a new algorithm proposed for detecting the randomicity and dynamic changes of time series, which can be used in the field of time series analysis. According to the PE theory, MEEMD algorithm is proposed and the fiber optic gyroscope signal is decomposed and reconstructed. Then, the low-order IMF terms of the mixed noise after decomposition is filtered and de-noised by the FLP algorithm. Finally, the signal processed by the MEEMD-FLP is reconstructed to get the result. The static test of a fiber optic gyroscope is carried out. The experimental results show that compared with the original fiber optic gyroscope signal, the RMSE after de-noising is reduced by 76.77%, and the standard deviation is reduced by 76.76%. It can effectively reduce the influence of noise on the fiber optic gyroscope output signal and has higher de-noising accuracy.
In a word, compared with the existing methods, the proposed method is applied to the original fiber optic gyroscope signal's adaptive analysis and de-noising modeling, which is completely adaptive instead of other man-made settings, such as the choice of wavelet basis function in the wavelet transform. It improved the de-noising accuracy of the system while reducing the influence of noise. We can predict that the method provides a new perspective for the analysis and de-noising of the fiber optic gyroscope's signal.