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.


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    Title :

    Ensemble Transfer Learning Midcourse Guidance Algorithm for Velocity Maximization


    Contributors:
    Jin, Tianyu (author) / He, Shaoming (author)

    Published in:

    Publication date :

    2023-04-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    LEARNING-BASED MIDCOURSE GUIDANCE FOR VELOCITY MAXIMIZATION WITH ANGULAR CONSTRAINT

    Jin, T. / Li, H. / He, S. et al. | British Library Conference Proceedings | 2021


    Near-Optimal Midcourse Guidance for Velocity Maximization with Constrained Arrival Angle

    Li, Hongyan / He, Shaoming / Wang, Jiang et al. | AIAA | 2021


    Simplified midcourse guidance techniques.

    Slater, G. L. / Stern, R. G. | NTRS | 1966



    SIMPLIFIED MIDCOURSE GUIDANCE TECHNIQUES

    SLATER, GARY / STERN, ROBERT | AIAA | 1966