Research on Signal Processing of MEMS Gyro Array

Abstract

A new random drift model and the measured angular rate model of MEMS gyro are presented. Based on such models, signal processing techniques are used to decrease gyro drift. Kalman filtering equations have been built for static measurement and dynamic measurement of the gyro array, which combines N individual gyros into a single rate estimate. By selecting the favorable cross correlation coefficient between individual gyros in the noise correlation matrix, the gyro array performance can be significantly improved over that of any individual component device. A new gyro array dynamic measurement procession is also presented. Data fusion of the difference between individual gyro dynamic measurements can identify every gyro real-Time drift out and get its noisy test. Based on the laws of the gyro curve motion, the tested dynamic signal is filtered to improve the gyro accuracy. All these processings have been implemented by digital signal processor. Simulation results show that the static drift can decrease from 22.1°/h to 0.184°/h and the dynamic drift can decrease from 22.1°/h to 8.98°/h.