Predicting trajectory of drogue based on multi-head convolutional long-short-term memory network

Abstract

Aerial refueling is an important technology of great military significance. It can effectively boost an aircraft's performance owing to the longer period of time and longer endurance of range an aircraft can maintain in the air. To solve the problem that it is hard for a receiver aircraft to track a drogue during its docking phase, a drogue trajectory prediction method based on the multi-head convolutional long-short-term memory network is proposed. First, the one-dimensional time sequence data of the drogue is extended to its high-dimensional space. Then its spatial features are extracted through the multi-head convolutional residual network and fused together. On this basis, a long-short-term memory network is adopted to reveal the underlying temporal correlations among the spatial features and predict the trajectory of the drogue. The simulation and experimental results show that the method presented in this paper has a higher prediction accuracy than the traditional prediction methods that use time sequence data.