Roll angle estimation using low cost MEMS sensors for paddy field machine

The agricultural machinery implements in paddy field are required to measure the roll angle dynamically. In order to reduce the effects of external acceleration on roll estimation thus improving dynamic measurement precision with low cost, this paper proposed an algorithm, the external acceleration roll angle Kalman filter (EARAKF), which estimated the external acceleration model with a one-order low pass filter and Gaussian white noise. After subtracting the estimated real roll angle from the value of accelerometers, the external acceleration superimposing on the single-axis accelerometer was estimated. Eventually, the roll angle estimation without external acceleration effect was obtained after the estimated external acceleration being subtracted from the Kalman filter (KF) in the measurement equation. Furthermore, a parallel track platform was used to impose external acceleration on the accelerometer. The test results shown that the acceleration on Y-axis increased significantly. The root mean square error (RMSE) of the EARAKF was 0.49°, while that of the KF algorithm was 1.91°, which demonstrated that the EARAKF was less influenced by the external acceleration. In the paddy field tests, the RMSE of the KF algorithm were figured out as 1.74°, while that of the EARAKF algorithm was 0.77°, which concluded that the EARAKF can estimate the external acceleration of the accelerometer and obtain a more accurate roll angle. The proposed algorithm presented a roll angle measurement method for the agricultural implements which working in paddy field dynamic scenario and could get less affected by the external acceleration with low cost and high accuracy.