Short-term Track Prediction of UAV Based on PSO-CNN-GRU Model
Aiming at UAV track prediction,in order to improve the convergence speed and accuracy of UAV track prediction,a PSO-CNN-GRU UAV track prediction model based on particle swarm optimization and convolutional neural network and gate re-current unit network is proposed in this paper.In order to solve the problem that it is difficult to optimize the super parameters in the traditional cyclic neural network,the parameters of GRU network such as hidden layer scale,learning rate and training batch size are optimized automatically by PSO algorithm to avoid forming a local optimal solution.For the extraction of historical key informa-tion and important features,the local dependence relationship between variables is extracted by CNN network to realize the mining of hidden features.The experimental results show that,compared with the original GRU model,MAE and MSE values of PSO-CNN-GRU model are reduced by 65.13%and 73.25%respectively,which has good accuracy and robustness.
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