Research on a Sowing Depth Detection System Based on an Improved Adaptive Kalman Filtering Method

The current real-time detection of sowing depth has the problems low accuracy and poor reliability. After analyzing movement mechanism monomer parallel four-bar linkage, an improved adaptive Kalman filtering method for is proposed. uses two MPU6050 attitude sensors. difference between rotation angle linkage used as relative to monomer. characteristics up–down translation, changes can be obtained by converting data. By using moving average filter, particle filter fuse data in positions perform MATLAB simulation, mean squared errors above four algorithms are 0.12645, 0.05545, 0.03785, 0.0189, respectively. In end, experiment was carried out, it found that algorithm detects smallest error, which 0.0636, track better. At same time, more adaptable noise obtains ideal effect.