基于卡尔曼滤波的陀螺仪阵列技术研究

多个相同型号的陀螺仪测量轴相互平行，测量同一个角速度信号所组成的阵列叫做陀螺阵列。通过研究陀螺阵列提高惯性测量精度的信息处理算法，建立单个微机电（MEMS）陀螺仪的两种不同漂移模型，利用Allan方差对漂移系数进行辨识，将辨识出的随机漂移系数应用于卡尔曼滤波。通过卡尔曼滤波将陀螺阵列的信息融合为一个较高精度的输出，证明了卡尔曼滤波的稳定性。通过实验对比了不同建模方法的优劣，并且验证了基于卡尔曼滤波的信息融合方法可以有效提高MEMS陀螺仪的精度。

Research on Gyroscope Array Based on Kalman Filter

The same type of measurement axes of gyroscope are parallel to each other. The information processing algorithms to improve the accuracy of inertial measurement via gyroscope array are researched. Two different drift models of single MEMS gyroscope are established, and the random drift coefficients are identified via Allan variance. Then the identified random drift coefficients are used for Kalman filter, and the muti-gyroscopes information is fused to acquire a highly precise output of gyroscope array through Kalman filter. The result proves the stability of Kalman filter. Finally, the advantages and disadvantages of different modeling methods are compared by experiment. The experimental results show that the accuracy of MEMS gyroscope can be improved effectively by the information fusion method based on Kalman filter.