Abstract: Aiming at the problem that the existing methods are difficult to predict the trajectory of the flying moving target with uncertain behavior patterns, a trajectory prediction method for flying moving targets based on inverse reinforcement learning is proposed, which can predict the moving trajectory by learning the behavior preference of the target and simulating the decision making process of the target behavior. Firstly, the behavior decision model and behavior preference model of the target are established based on deep neural networks, and then the model parameters are alternately learned by a maximum entropy inverse reinforcement learning method. In order to effectively learn the uncertain behavior characteristics of the target, the supervised learning method is used to learn the probability distribution model of the target sample trajectories, which are then used to guide the training of the target behavior preference model and initialize the target behavior decision model. Meanwhile, the training quality of the target behavior preference model is improved by pre training. The simulation results show that the proposed method can accurately simulate the behavior patterns of the target through the learned target behavior decision model, and the similarity between the predicted target trajectory distribution and the real target trajectory distribution under Kullback Leibler (KL) divergence can reach 0.24.

Key words: Trajectory prediction, Flying moving targets, Uncertain behavior patterns, Inverse reinforcement learning, Deep neural networks