Research on MEMS-IMU Attitude Estimation Based on the Kalman Method

MEMS-IMU technology is mature and widely used. However, the drawback of large noise remains a key factor restricting the large-scale application of MEMS-IMUs. In this paper, we study the data fusion methods for IMUs based on two types of Kalman logic, the Extended Kalman Filter (EKF) and the Error-State Kalman Filter (ESKF). We attempt to improve the angular rate bias estimation in the ESKF by applying a filtering window. Through a comparison of the two Kalman filtering methods using actual measurement data, we analyze the improvement effects and explore their performance under noise interference. The results show that both EKF and ESKF can effectively suppress noise, and the improvement with the filtering window enhances the stability of bias estimation.