Dynamic weighing algorithm for unmanned aerial vehicles based on filtering fusion

In agriculture, obtaining the remaining fertilizer weight is crucial for achieving accurate fertilizer application when using drones for spreading fertilizer. To address the problem of inaccurate weighing of remaining fertilizer caused by environmental and system factors during the fertilizer application process, a fusion filtering algorithm suitable for dynamic weighing of fertilizer is proposed. First, a second-order model is established between the wheel speed and the fertilizer application rate. The three coefficients in the second-order mapping relationship formula between the fertilizer weight and the wheel speed are used as the four-dimensional state variables of the Kalman filter, with the wheel speed on the drone set as the control variable. Then the weight data measured by the weighing sensor is sent to the Kalman filter for primary filtering. At the same time, the sliding average filtering algorithm is used to smooth out the oscillation phenomenon in the filtering results. Simulation experiments and real data processing results show that the drone dynamic weighing fusion algorithm, based on the four-dimensional Kalman filter and sliding average filtering has good noise reduction and smoothing effects on fertilizer weight data.