Abstract:

To solve the problem that trajectory prediction is difficult to maintain high prediction accuracy and short prediction time in autonomous air combat, an AdaBoost particle swarm optimization for long and short term memory network prediction method is proposed. Firstly, the dynamic model of unmanned aerial vehicle with three degrees of freedom is established to solve the problem of data source of maneuver trajectory. Secondly, the long and short term memory network is analyzed, and the sliding module input matrix of online prediction is introduced. Particle swarm optimization algorithm is used to replace the traditional back propagation algorithm through time to update the internal weights of the network. Meanwhile, in order to solve the problem of non-orientation of the optimization algorithm, a data sharing method is proposed. Then, in order to further improve the prediction accuracy, AdaBoost algorithm is used to build the outer framework, and the prediction accuracy and prediction time are balanced by controlling the number of weak predictors. Finally, compared with five neural network prediction methods in a period of more frequent changes in the trajectory prediction, the results show that the proposed method can meet the requirements of accuracy and time.

Key words: trajectory prediction, particle swarm optimization for long and short term memory network, dynamic model, unmanned aerial vehicle