This paper proposes an ensemble transfer learning guidance algorithm for angular-constrained midcourse guidance to maximize the terminal velocity. The algorithm developed improves the generalization capability of the trained deep neural network to adapt to a new environment. First several deep neural guidance networks are trained for some specific working environments via supervised learning. A small-scale ensemble transfer learning network is then leveraged to fuse the knowledge of different pretrained deep neural network. This requires much less labeled data to transfer existing knowledge to a new working environment and hence greatly improves the learning efficiency, compared to the supervised learning philosophy. Extensive numerical simulations are performed to demonstrate the effectiveness of the proposed algorithm.
Ensemble Transfer Learning Midcourse Guidance Algorithm for Velocity Maximization
Journal of Aerospace Information Systems ; 20 , 4 ; 204-215
2023-04-01
Article (Journal)
Electronic Resource
English
LEARNING-BASED MIDCOURSE GUIDANCE FOR VELOCITY MAXIMIZATION WITH ANGULAR CONSTRAINT
British Library Conference Proceedings | 2021
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