Original data and denoising data of MESM gyroscope and MPU6050

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. This paper proposes 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). 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 according to their sample entropy (SE). Finally, the mixed component is filtered by GRU-UKF and combined with the useful component to reconstruct the denoised signal. In the validation experiment, the bias instability of MEMS gyroscope signal is reduced from 0.375°/h to 0.016°/h, and the standard deviation suppression rate reaches 89.28%, which prove the effectiveness and superiority of the proposed method.

This is the raw data and denoising data of the MEMS gyroscopes and MPU6050 that we processed in the validation experiment.