Effective recognition of radar jamming is of great importance in improving radar system's anti-jamming capability. Existing radar jamming recognition methods based on convolutional neural networks suffer from limitations such as low recognition accuracy and poor network stability in complex electromagnetic environments. This is due to incomplete extraction and utilization of useful information from the jamming signal. In this article, a few-shot adaptive confidence aggregation and cross-modal refinement jamming recognition (JR-ACAR) method is designed to mine the complete information of the jamming signal for enhancing both accuracy and robustness of the method. An adaptive confidence aggregation model is proposed to fully exploit the complementary information between the modulus, phase, and real and imaginary parts of the input time–frequency spectrum of jamming signal and adaptively aggregate this information by confidence vectors. Moreover, to further improve the recognition accuracy while enhancing the robustness of the network, a cross-modal refinement model is proposed to mine the correlation information between the aggregated jamming recognition result and the original input time–frequency image. Experimental results on both simulated and measured mixed dataset validate that the proposed JR-ACAR method is superior to the existing methods for radar jamming recognition in terms of recognition accuracy and robustness.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Few-Shot Radar Jamming Recognition Network via Complete Information Mining


    Beteiligte:
    Luo, Zhenyu (Autor:in) / Cao, Yunhe (Autor:in) / Yeo, Tat-Soon (Autor:in) / Wang, Fengfei (Autor:in)


    Erscheinungsdatum :

    01.06.2024


    Format / Umfang :

    7348737 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Transfer Learning-Based Dual GCN for Radar Active Deceptive Jamming Few-Shot Recognition

    Wu, Zhenhua / Wang, Tengxin / Cao, Yice et al. | IEEE | 2025


    Recognition of Radar Compound Jamming Based on Convolutional Neural Network

    Zhou, Hongping / Wang, Lei / Guo, Zhongyi | IEEE | 2023


    Radar Network Target Tracking in a Jamming Environment

    Song, X. / Sun, Z. / IEEE; Dayton Section et al. | British Library Conference Proceedings | 1996


    Radar network target tracking in a jamming environment

    Song Xiaoquan / Sun Zhongkang | IEEE | 1996


    Radar anti-jamming Techniques

    Maksimov, M.V. | TIBKAT | 1980