Near-space hypersonic vehicles are characterized by fast flight speed, good maneuverability and strong surprise capability, which make it a great challenge to predict and intercept such targets. Aiming at the problem of insufficient mining of semantic information and spatial structure information among features in the trajectory prediction process, a trajectory prediction algorithm integrating the multi-attention mechanism and the encoder-decoder structure based on LSTM is proposed. Firstly, the kinematic analysis of the hypersonic vehicle is carried out to construct the vehicle trajectory database under six-dimensional features, and then the encoder and decoder fusing the multi-head attention mechanism and LSTM neural network are designed. On this basis, the time-step data is changed to make the network output the trajectory sequence in the future period. The experimental results show that the model improves 29% and 13% in the two metrics of MAE and MAPE compared with similar methods, and realizes the effective prediction of multiple flight trajectories of hypersonic vehicles.
Hypersonic Vehicle Trajectory Prediction Algorithm Incorporating Multiple Attention and Coding-Decoding Structures
17.11.2023
718575 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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