A UAV Air Combat Trajectory Prediction Method Based on QCNet

Abstract

The Unmanned Aerial Vehicle (UAV) air combat trajectory prediction algorithm facilitates strategic pre-planning by predicting UAV fiight trajectories with high accuracy, thus mitigating risks and securing advantages in intricate aerial scenarios. This study tackles the prevalent limitations of existing datasets, which are often restricted in scale and scenario diversity, by introducing a novel UAV air combat trajectory prediction methodology predicated on QCNet. Firstly, a robust UAV air combat dynamics model is developed to synthesise air combat trajectories, forming the basis for a comprehensive trajectory prediction dataset. Subsequently, a specialised trajectory prediction framework utilising QCNet is devised, followed by rigorous algorithm training. The parameter impact analysis is conducted to assess the infiuence of critical algorithm parameters on efficiency. The results of the parameter impact analysis experiment indicate that augmenting the number of encoder layers and the decoder's recurrent steps generally enhances performance, albeit an excessive increment in recurrent steps may inversely affect efficiency. Finally, the proposed algorithm is evaluated compared with other traditional time-series prediction algorithms and shows better performance. The effectiveness experiment indicates that the proposed algorithm can predict the fiight trajectories of UAVs and provide corresponding probabilities under different manoeuvres.

Keywords

artificial intelligence / deep learning / feature extraction / time series / vehicle guidance

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Funding

authors would like to acknowledge the National Natural Science Foundation (NSF) of China(Grant 61976014)

Innovation Research Foundaton of COMAC-BUAA Aircraft Research Institute(Grant 24010305)