IMU Accelerometer Data Fitting Method for Attitude Tracking

A nonlinear least squares optimization method based on time series has been proposed to address the issue of errors in the Inertial Measurement Unit (IMU) accelerometer under high-frequency operating conditions during bead frame and nozzle attitude following monitoring. By combining the trajectory of the rigid body and the curve of acceleration data, a nonlinear model of the acceleration data curve is first fitted using the Lomb-Scargle method; then, the nonlinear least squares optimization method is used to obtain the optimal nonlinear model; finally, the acceleration values derived from the model are used to calculate the attitude of the rigid body. The experimental results shown that, compared to the traditional method of error compensation for acceleration data, the method of fitting the acceleration data curve can effectively avoid the impact of noise and data drift on the acceleration data, while also improving the precision and stability of the calculated attitude angle.