In the domain of electronic warfare, radar signal deinterleaving emerges as the foundational and indispensable phase of electronic reconnaissance. The ever-increasing complexity of electromagnetic environments, further compounded by technological advancements, such as multifunction radars (MFRs), has led to the inadequacy of traditional deinterleaving techniques. To tackle these challenges, this article introduces the temporal convolutional attention network (TCAN) framework. This framework harmoniously combines a TCN with an advanced attention mechanism, thereby significantly enhancing the system's signal sorting proficiency. Through rigorous experimental validation, we demonstrate that TCAN consistently outperforms existing baseline methods. This superiority is particularly pronounced under conditions of signal sparsity and in nonideal environments, which are typified by pulse loss, spurious pulses, and measurement errors. Furthermore, we conduct a thorough analysis to elucidate the profound impact of various input formats and multiparameter features on deinterleaving performance. By meticulously examining these factors, we establish TCAN as a robust and versatile solution capable of effectively navigating the heightened complexity of modern radar signal environments. Our findings highlight TCAN's potential as a potent instrument for augmenting electronic reconnaissance capabilities amidst evolving electromagnetic challenges.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deinterleaving of Intercepted Radar Pulse Streams via Temporal Convolutional Attention Network


    Beteiligte:
    Wang, Haojian (Autor:in) / Tao, Zhenji (Autor:in) / He, Jin (Autor:in) / Shu, Ting (Autor:in)


    Erscheinungsdatum :

    01.08.2025


    Format / Umfang :

    5083274 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deinterleaving of Pulse Streams With Denoising Autoencoders

    Li, Xueqiong / Liu, Zhangmeng / Huang, Zhitao | IEEE | 2020


    Deinterleaving of Pulse Streams With Conditional Autoregressive Kernel Mixture Network

    Cong Feng, Han / Li Jiang, Kai / Zhou, Zhixin et al. | IEEE | 2025


    Deep ToA Mask-Based Recursive Radar Pulse Deinterleaving

    Xiang, Haoran / Shen, Furao / Zhao, Jian | IEEE | 2023



    Incremental Deinterleaving of Radar Emitters

    Scholl, Stefan / Bruggenwirth, Stefan | IEEE | 2023