On Improving the Accuracy of Micromachined Gyroscopes Based on Multi-Sensor Fusion

Current state-of-the-art micromachined gyroscopes can not compete with the established sensors in high-accuracy application areas such as guidance and inertial navigation. In this paper one method based on homogeneous multi-sensor fusion was presented to improve the accuracy of the micromachined gyroscopes. In this method several gyroscopes of the same kind were combined into one single effective device through Kalman filtering, the performance of which would surpass that of any individual sensor. The secret of the performance improving lies in the optimal estimation of the random noise sources such as rate random walk and angular random walk for compensating the measurement values. Especially, the cross correlation between the noises of the same type from different gyroscopes was used to establish the system noise covariance matrix and the measurement noise covariance matrix for Kalman filtering to improve the performance further. On the other hand, contrasted with the current static filter design we firstly proposed one difference modeling method to establish the dynamic filter to satisfy the optimal estimation in the situation with angular rate input, in which the mutual subtraction of the measurement values between every two gyroscopes in the sensor array could avoid the trouble of obtaining the true rate. The experiments showed that three gyroscopes with bias drift of 35 degree per hour were able to be combined into one virtual gyroscope with drift of 0.15 degree per hour and 20 degree per hour through the presented static filter and dynamic filter respectively. The multi-sensor fusion method is really capable of improving the accuracy of the micromachined gyroscopes, which provides the possibility of using these low cost MEMS sensors in high-accuracy application areas.