基于多重渐消因子变分贝叶斯的陀螺阵列融合算法

针对由多个MEMS陀螺仪组成的阵列系统在动态情况下噪声时变导致输出精度低的问题,提出了新的动态滤波模型和滤波方法。通过分析MEMS陀螺的误差特性和对角速度进行动态建模,构建了基于角速度估计的阵列陀螺随机误差动态滤波模型。由于动态情况下模型的不确定性导致传统方法精度较差,设计了一种多重渐消因子变分贝叶斯自适应卡尔曼滤波算法,利用变分贝叶斯思想和强跟踪理论提高了滤波器量测噪声估计精度、收敛速度和鲁棒性。最后在高精度转台上进行了静态实验和动态实验。实验结果表明：在静态条件下,“虚拟陀螺”方差降低为单个陀螺的4%,零偏不稳定性降低为47.2%;在动态条件下,“虚拟陀螺”能有效跟踪角速度的变化且角速度残差方差降低为单个陀螺的6.2%。该滤波算法能有效提高MEMS陀螺阵列系统的输出精度。

Gyro array fusion algorithm based on multiple fading factors and variational bayesian

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.