一种基于迁移学习的多任务制导算法

For typical aircraft guidance missions, the deep learning algorithm can be used to effectively fit the functional relationship between missile flight state and guidance command. However, when the guidance mission changes, the mapping relationship between them will also change. As a result, a pretrained model in the current environment cannot directly act on a new environment, and retraining the guidance model requires a large amount of ballistic data and a huge amount of time cost. In order to solve the above problems, a domain adversarial neural network is introduced based on the idea of transfer learning, and a multitask guidance algorithm based on transfer learning is proposed. One task in the source domain containing a large amount of tag data is used to assist two tasks in the target domain containing a small amount of tag data for transfer learning, so as to overcome the environmental difference between pre-training and online control. The key features that are not sensitive to the task environment are extracted by using feature extractor and domain discriminator so that the neural network learn the underlying information shared by each task. In order to improve the prediction accuracy, the bias acceleration predictors for different tasks are designed, respectively. The simulated results show that the multitask guidance algorithm based on transfer learning can predict the acceleration instruction of a missile in different missions.