基于LSTM的弹道导弹主动段轨迹预报

Long term trajectory prediction for boost-phase ballistic missile (BM) can provide early warning information for the missile defense system. Traditional trajectory prediction methods mostly focus on the BM's coast and reentry phases, inferring the target state at future time through analytical, numerical integration or function approximation methods. In contrast, the boost-phase trajectory prediction is more challenging because there are many unknown forces acting on the BM during this stage. To this end, a long short-term memory (LSTM) network based boost-phase BM trajectory prediction method is proposed in this paper. Specifically, large-scale trajectory samples for the network training are generated first according to the dynamic model of the boost-phase BM and the typical ballistic parameters. Next, a recursive trajectory prediction method for the boost-phase BM based on deep LSTM network is designed. Finally, simulation results compared with the numerical integration, polynomial fitting and back propagation neural network based trajectory prediction methods show the superiority of the proposed method in long term boost-phase BM trajectory prediction.