Multi-Frame Vibration MEMS Gyroscope Temperature Compensation Based on Combined GWO-VMD-TCN-LSTM Algorithm

This paper presents a temperature compensation model for the Multi-Frame Vibration MEMS Gyroscope (DMFVMG) based on Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD) for denoising and a combination of the Temporal Convolutional Network (TCN) and the Long Short-Term Memory (LSTM) network for temperature drift prediction. Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model significantly enhanced the gyroscope's performance across various temperatures, reducing the rate random wander from 102.929°/h/√Hz to 17.6903°/h/√Hz and the bias instability from 63.70°/h to 1.38°/h, with reductions of 82.81% and 97.83%, respectively. This study validates the effectiveness and superiority of the proposed temperature compensation model.