Aiming at the problems of low output precision and poor tracking performance of an array system composed of multiple MEMS gyroscopes under dynamic conditions, a new dynamic filtering model and filtering method were proposed. By analyzing the error characteristics of the MEMS gyroscope and modeling the angular velocity dynamically, a random error dynamic filtering model of the array gyroscope based on the angular velocity estimation was constructed. Due to the uncertainty of the model in the dynamic situation, the tracking performance of the traditional method is degraded. A multiple fading factor adaptive Kalman filter based on variational Bayesian method algorithm was designed. The variational Bayesian method and strong tracking theory were used to improve the estimation accuracy, convergence speed and robustness. Finally, the static and dynamic experiments were carried out on the high-precision turntable. The experimental results show that under static conditions, the variance is reduced to 4% of a single gyro, and the zero bias instability is reduced to 47.2%; Under the dynamic condition, it can effectively track the change of angular velocity, and the angular velocity residual variance is reduced to 6.2% of that of a single gyro. This algorithm can effectively improve the output accuracy of MEMS Gyro array system.