Error State Kalman Filter for Integrated Attitude Estimation Based on Data Fusion of MIMU Inertial Array and...

Attitude estimation has increasingly relied on MEMS inertial measurement units (IMUs) owing to the low cost and miniature size, but the inherent high random noise and error accumulation limit long-term measurement accuracy. This article proposes an integrated attitude estimation algorithm based on data fusion from a MIMU inertial array and magnetometer to address this challenge. First, a redundant inertial array is constructed using homogeneous gyroscopes, and a Kalman filter (KF) is designed to fuse output signals from multiple gyroscopes to estimate true angular rate. Second, an integrated error state Kalman filter (ESKF) for the MIMU/magnetometer system is developed. Using the attitude quaternion calculated by the strapdown inertial solution as the nominal state and combining it with measurements from the accelerometer and magnetometer as observations, the attitude error is estimated and corrected. Both simulations and field experiments were conducted to validate the effectiveness of the proposed algorithm. The experimental results show that the ESKF algorithm performs best in estimation accuracy and addresses the issue of error accumulation and fluctuation. In particular, the Root Mean Square Error (RMSE) of the ESKF algorithm was significantly reduced, with the roll angle reduced by 55.34%, the pitch angle by 30.25%, and the yaw angle by 55.71%.