Deinterleaving emitters with complex patterns presents a significant challenge for electronic support measure systems. In this article, we address this issue by formulating deinterleaving as the optimization of an autoregressive likelihood function. We then propose the conditional autoregressive kernel mixture network, a conditional generative model that estimates the conditional density of pulse parameters based on previous noisy observations and source labels to estimate the solution of this optimization problem. The model, trained with a modified loss function for denoising, can extract pulses belonging to the class of a given source label. Therefore, the denoising-based deinterleaving is achieved with a single model. We evaluate our model with the proposed algorithms on a challenging synthetic dataset under various nonideal conditions and compare it against existing approaches for both conventional and open-set deinterleaving. The results indicate that our method significantly outperforms the comparative techniques, especially in open-set deinterleaving.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deinterleaving of Pulse Streams With Conditional Autoregressive Kernel Mixture Network


    Beteiligte:
    Cong Feng, Han (Autor:in) / Li Jiang, Kai (Autor:in) / Zhou, Zhixin (Autor:in) / Zhao, YuXin (Autor:in) / Tian, KaiLun (Autor:in) / Tang, Bin (Autor:in)


    Erscheinungsdatum :

    01.04.2025


    Format / Umfang :

    2792353 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deinterleaving of Pulse Streams With Denoising Autoencoders

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



    Online Pulse Deinterleaving With Finite Automata

    Liu, Zhang-Meng | IEEE | 2020

    Freier Zugriff

    Time-period Analysis for Pulse Train Deinterleaving

    Nishiguchi, K. | British Library Online Contents | 2004