Random error modelling and compensation of accelerometer in airborne remote sensing stabilized platform

This paper studies the impact of the accelerometer output error on the levelling accuracy in levelling mode of the airborne remote sensing stabilized platform. On the basis that the accelerometer output signal is non-stationary, a non-stationary time series model of ARIMA(3,0,1) is established, with which an adaptive Kalman filter is designed. In the Kalman filter, an online correction method for the forgetting factor based on real-time measurements is presented, achieving the automatic adjustment of the Kalman filter gain. The model of the accelerometer and the adaptive Kalman filter are applied to the airborne remote sensing stabilized platform principle prototype made by our research group. On one hand, the results show that the model is fit for the accelerometer. On the other hand, it is noted that the new filter has improved the measurement accuracy of the accelerometer, depressed the oscillations of the levelling progress of the platform and reduced the steady-state error of the platform. The levelling performance of the stabilized platform is efficiently improved after all these work.