Angular disturbance prediction for countermeasure launcher in active protection system of moving armored vehicle based on an ensemble learning method

The active protection system (APS), usually installed on the turret of armored vehicles, can significantly improve the vehicles’ survivability on the battlefield by launching countermeasure munitions to actively intercept incoming threats. However, uncertainty over the launch angle of the countermeasure is increased due to angular disturbances when the off-road armored vehicle is moving over rough terrain. Therefore, accurate and comprehensive angular disturbance prediction is essential to the real-time monitoring of the countermeasure launch angle. In this paper, a deep ensemble learning (DEL)-based approach is proposed to predict the angular disturbances of the countermeasure launcher in the APS based on previous time-series information. In view of the intricate temporal attribute of angular disturbance prediction, the sampling information of historical time series measured by an inertial navigation device is adopted as the input of the developed DEL model. Then, the recursive multi-step (RMS) prediction strategy and multi-output (MO) prediction strategy are combined with the DEL model to perform the final angular disturbance prediction for the countermeasure launcher in the APS of a moving armored vehicle. The proposed DEL model is validated by using the different datasets from real experiments. The results reveal that this approach can be used to accurately predict angular disturbances, with the maximum absolute error of each DOF less than 0.1°.