In the modern battle, information acquisition is the key for combat success and the reconnaissance is one of the main measures. Aiming at the systematic development of combat units, the reconnaissance mission is usually achieved by multi-platform cooperation. Airborne sensors, as the essential equipment to obtain battlefield information, are coordinated effectively for reaching the operation aim. There are two types of cooperative control strategies, short-sighted and non-short-sighted ones. In the process of strategy optimization, the former only aims to maximize the current immediate return, but ignores the long-term return. In addition, active sensors continuously radiate electromagnetic waves outward when obtaining continuous measurement, which is easy to expose their own position. Therefore, how to improve their ability to survive is particularly important. To this end, considering the target threat, the airborne multi-platform collaborative detection method is proposed based on reinforcement learning, which takes into account the current immediate return as well as the future long-term return, and aims to maximize information perception under the premise of self-security. The simulation tests demonstrate the effectiveness of this method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Airborne Multi-platform Sensor Scheduling Based on Reinforcement Learning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Yu, Xiang (editor) / Wang, Yuedong (author) / He, Jing (author) / Yan, Shi (author) / Liang, Yan (author)


    Publication date :

    2021-10-30


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Airborne Multi-platform Sensor Scheduling Based on Reinforcement Learning

    Wang, Yuedong / He, Jing / Yan, Shi et al. | TIBKAT | 2022


    Airborne Multi-platform Sensor Scheduling Based on Reinforcement Learning

    Wang, Yuedong / He, Jing / Yan, Shi et al. | British Library Conference Proceedings | 2022




    Reinforcement Learning in Scheduling

    United States; National Aeronautics and Space Administration / United States; Air Force | British Library Conference Proceedings | 1993