A Denoising Method for MEMS Gyroscope Based on ICELMDAN and GRU-UKF

As an important component of inertial guidance and navigation, micro-electro-mechanical-system (MEMS) gyroscope is widely used in many fields. However, the accumulation of noise errors limits the long-term accuracy and further application of MEMS gyroscope. In this paper, a novel denoising method for MEMS gyroscope based on interpolated complementary ensemble local mean decomposition with adaptive noise (ICELMDAN) and gated recurrent unit-unscented Kalman filter (GRU-UKF) has been proposed. First, the original signal of MEMS gyroscope is decomposed into multiple product functions (PFs) by ICELMDAN. Second, the PFs are classified into useful component, mixed component, and noise component based on the corresponding sample entropies (SEs). Finally, the mixed component is filtered by GRU-UKF and combined with the useful component to reconstruct the denoised signal. Experiments are carried out to validate the proposed method. The bias instability of MEMS gyroscope is reduced from 0.375°/h to 0.016°/h, and the standard deviation suppression rate reaches 89.28%, demonstrating the effectiveness and superiority of the proposed method.