A Novelty Temperature Compensation Model for Dual-Mass Vibration MEMS Gyroscope Based on Machine Learning and TTAO-VMD Algorithm.

The output of MEMS gyroscopes is highly vulnerable to ambient temperature variations, which induce temperature drift errors and degrade navigation precision. Consequently, temperature compensation for MEMS gyroscope outputs is of critical importance. To address this issue, this study proposes a novel temperature compensation model for the dual-mass vibration MEMS gyroscope (DMVMG), which integrates the TTAO-VMD, 1D-CNN-Bi-GRU-Attention, and SHAKF algorithms. The implementation process of the proposed model is as follows: firstly, the structural configuration and fundamental operating principle of the DMVMG are elaborated. Secondly, the temperature error compensation model is constructed based on the fusion of the TTAO-VMD, 1D-CNN-Bi-GRU-Attention, and SHAKF algorithms. Thirdly, the raw output signal of the DMVMG is preprocessed using the TTAO-VMD algorithm, which decomposes the signal into four distinct components, namely high-frequency noise, white noise, mixed noise, and temperature-induced noise. Subsequently, the high-frequency and white noise components are eliminated, while the mixed noise component is filtered via the SHAKF algorithm. On this basis, the 1D-CNN-Bi-GRU-Attention algorithm is adopted to establish the temperature error compensation model, with the temperature, temperature change rate, time, and temperature-induced noise as input variables. Finally, the optimized signal components are reconstructed to yield the temperature-compensated output of the DMVMG. The experimental results based on the Allan variance method demonstrate that the angle random walk (N) is reduced from 18.56 °/h to 0.17 °/h, and the bias instability (B) is decreased from 32.76 °/h to 0.82 °/h, verifying the effectiveness of the proposed method.