To address the challenges of limited interpretability and scarce battlefield situational data in current deep learning-based models for combat intention recognition (CIR), this paper proposes a novel knowledge enhanced network (KEN). The proposed KEN formalizes expert knowledge using knowledge graph. By integrating the knowledge representation module and knowledge alignment module, the proposed network can introduce knowledge into any recognition model. Leveraging the prior knowledge, our KEN alleviates the performance limitations resulting from data scarcity. Furthermore, the knowledge alignment module could present an intuitive visualization of the degree of attention that samples allocate to different knowledge elements, thereby greatly enhancing the interpretability of the model. Experimental results show the superiority of our KEN, e.g. achieving around 95.63% accuracy on typical combat scenarios, outperforming other baseline models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Novel Knowledge Enhanced Network for Few-Shot Combat Intention Recognition


    Contributors:
    Wang, Xuan (author) / Jin, Benzhou (author) / Jia, Mingyang (author) / Zhang, Xiaofei (author)


    Publication date :

    2024-10-18


    Size :

    3015865 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Tactical Intention Recognition Method of Air Combat Target Based on BiLSTM network

    Wang, Xingyu / Yang, Zhen / Zhan, Guang et al. | IEEE | 2022


    Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat

    Meng Guanglei / Zhao Runnan / Wang Biao et al. | DOAJ | 2021

    Free access

    A Review of Methods of Battlefield Target Combat Intention Recognition

    Xu, Hongfeng / Zhao, Jiajia / Chen, Linxiu et al. | Springer Verlag | 2023


    INTENTION RECOGNITION

    European Patent Office | 2017

    Free access

    Intention recognition

    AL-DAHLE AHMAD / HEXSEL BRUNO A | European Patent Office | 2022

    Free access