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Trajectory prediction algorithm of ballistic missile driven by data and knowledge
Hongyan Zang (Harbin Institute of Technology), Changsheng Gao (Harbin Institute of Technology), Yudong Hu (Harbin Institute of Technology), Wuxing Jing (Harbin Institute of Technology)
Abstract: Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge (DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning (T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a data-driven state parameter mapping (SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.