由于导弹所处的气动环境等外界条件复杂多变，因此在特定气动环境中表现优异的深度监督学习制导算法将无 法直接作用于新环境，这给制导指令的精准预测带来了很大挑战。针对上述问题，本文以落角约束下优化飞行时间的制 导问题为背景，提出了一种基于迁移学习的角度约束时间最短制导算法，该算法只需极少量的新数据，便能使制导律迅 速适应不同的气动环境。在本文中，我们首先通过域对抗地训练特征提取器和域判别器，从不同气动环境的弹道数据中 提取出对气动变化不敏感的关键特征;其次设计了适应于不同气动环境的偏置加速度预测器，进而实现了对导弹的精准 制导;最后，大量数值仿真结果表明，在新气动环境下，基于迁移学习的角度约束时间最短制导算法仍能实现制导指令 的精准预测。

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 problems, this paper proposes a Transfer-learning-based Impact-angel Constraint with Time-minimum Guidance algorithm (TICTG) for missile midcourse guidance which minimizes 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 feature extractor and domain dis-criminator against domain ground. Secondly, we also design bias acceleration predictor adapted to different aero-dynamic conditions, so as to achieve accurate guidance of the missile. A large number of numerical simulation results show that this method can still achieve accurate guidance instruction prediction in the new aerodynamic environment.