Abstract:The random error of MEMS gyroscope has become the main factor affecting system navigation accuracy, and there is a universality problem in modeling, so we propose an error compensation method of cubic Kalman iterative estimation. The Allan variance is adopted to identify the random error parameters of MEMS gyroscope. Combining the trailing property of MEMS gyroscope output data autocorrelation and partial correlation function, we design a Kalman filter model of MEMS gyroscope error based on time series ARMA. The test environment is set up with 6 MEMS inertial measurement units of the same model, and the verification experiment is carried out. The results show that the error coefficients of the MEMS gyroscope after the filtering process are significantly reduced, and the filtering effect is enhanced with the increased filtering. Meanwhile, the 6 sets of MEMS test equipment show the consistency of the error reduction trend. The comparison of results of attitude errors caused by gyroscope show that the filter method proposed in this paper is helpful for navigation.
SUN Wei,SONG Ruyi,WANG Yuhang et al. Universality Verification of Random Error Compensation Method for MEMS Gyroscope with Kalman Filter[J]. jgg, 2019, 39(11): 1188-1193.