High-Precision Attitude Measurement Based on Differential MEMS Sensor Array Structure and Improved ESKF Algorithm

High-precision attitude measurement is widely used in aerospace, robotics, medical imaging and other fields. The precision of attitude measurement mainly depends on MEMS inertial sensor array technology and advanced attitude solution algorithms, but the existing sensor array design and attitude solution algorithms can not effectively deal with the measurement noise. To solve the above problems, an attitude measurement system based on differential sensor array structure is designed in this paper, and an Improved Error State Kalman Filter (IESKF) algorithm based on residual extraction and analysis technology is proposed. The experimental results show that both the differential sensor array and IESKF algorithm can effectively improve the accuracy of attitude measurement. The angular velocity error of the differential array is improved by about 79.4% compared with that of a single sensor, and that of a group of differential structure sensors is improved by about 31.4% compared with that of a dual sensor. The quaternion output accuracy of IESKF algorithm is better than that of the general error state Kalman filtering algorithm.