基于改进 EKF 的 IMU 动态误差抑制.

The data accuracy and anti-interference of inertial measurement unit (IMU) three-axis Euler angle are affected by high frequency noise and strong instantaneous interference. To solve this problem, this paper proposes a dynamic error suppression method with low computational burden, good real-time performance and low cost, which is more suitable for embedded systems. In this method, an infinite impulse response-extended Kalman filter (IR-EKF) is introduced to the front end of the EKF algorithm. The data are preprocessed with a second-order low-pass Butterworth filter (BF) to help EKF suppress high frequency or strong interference. The IIR-EKF algorithm is implemented in the STM32H743 microcontroller. The experimental results show that the data variance is very big when the EKF acts alone, and great discrepancy occurs when strong interferences are encountered. When unscented Kalman filter (UKF)acts alone, the data variance is smaller than that of EKF. Although it does not depend on the initial noise parameters, it does not meet the requirements. After the addition of the second-order BF, the data variance is significantly reduced, the instantaneous error is greatly suppressed, and the stability and anti-interference ability of the system are enhanced. [ABSTRACT FROM AUTHOR]