Comparison of Attitude Estimation Algorithms With IMU Under External Acceleration

Parikh, Dhruv Piyush; Vohra, Sajil; Kaveshgar, Maryam

doi:10.1109/ises52644.2021.00037

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Although its filtering effect is better than traditional filters, the training model of this method is complex and difficult to be widely applied in practical applications. Parikh D compared the performance of different attitude calculation algorithms under vibration conditions and concluded that the Mahony complementary filter algorithm and the Kalman filter algorithm have better robustness compared to the Madgwick algorithm and traditional complementary filter algorithm under vibration conditions [14]. In order to better address the non-stationary heavy-tailed measurement noise (NSHTMN) caused by changes in the internal or external environment of a complex system, some scholars have made improvements to the Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

Lan,

Wang,

Song

et al. 2024

Meas. Sci. Technol.

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The error characteristics of a typical commercial MEMS-IMU were analyzed. Using 15 ICM-42688 sensors, a 3*5 MEMS-IMU array was designed, and a weighted data fusion method based on Allan variance for the MEMS-IMU array was proposed. This effectively reduces the random measurement errors of the MEMS-IMU, providing a data foundation for the precise measurement of motion and attitude of robots, vehicles, aircraft, and other systems under low-cost conditions. The text describes a measurement method for non-stationary motion attitudes of sports vehicles based on adaptive Kalman-Mahony. Specifically, it first uses adaptive Kalman filtering on array sensor data to calculate the measurement noise in real-time and adaptively adjust the filtering gain. Then, it determines the compensation coefficient of the accelerometer to the gyroscope angular velocity based on the motion state of the vehicle, and solves the attitude through complementary filtering to obtain the motion attitude quaternion. Finally, it converts it into the roll angle, pitch angle, and yaw angle of the sports vehicle. Experimental results show that the proposed MEMS-IMU array weighted data fusion method based on Allan variance has significant advantages over traditional single MEMS-IUM methods and traditional average weighting methods in reducing sensor angle random walk and zero drift instability. The proposed adaptive Kalman-Mahony attitude measurement method also shows a significant improvement in the accuracy of non-stationary motion attitude measurement compared to traditional methods.

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Section: Introductionmentioning

confidence: 99%