关键词: 计算制导, 深度学习, 迁移学习, 落角约束, 偏置比例导引律

Abstract:

The aerodynamic environment and other external conditions of the missile are complex and changeable. The deep supervised learning guidance algorithm with excellent performance in the specific aerodynamic environment cannot be directly applied to the new environment, which brings great challenges to the accurate prediction of guidance instructions. To solve the above problem, this paper proposes a Transfer-learning-based Impact-angel Constraint with Time-minimum Guidance algorithm (TICTG) for missile guidance, which minimizes the impact time under impact angle constraints. The proposed algorithm can quickly adapt the guidance law to different aerodynamic conditions with very little new data. Firstly, we extract key features insensitive to aerodynamic changes from ballistic data in different aerodynamic environments by training the feature extractor and domain discriminator against the domain ground. Secondly, we design a bias acceleration predictor adapted to different aerodynamic conditions, so as to achieve accurate guidance of the missile. A large number of numerical simulation results show that the method proposed can achieve accurate prediction of guidance instructions in the new aerodynamic environment.

Key words: computational guidance, deep learning, transfer learning, impact angle constraint, bais proportion navigation