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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deinterleaving of Pulse Streams With Conditional Autoregressive Kernel Mixture Network


    Contributors:
    Cong Feng, Han (author) / Li Jiang, Kai (author) / Zhou, Zhixin (author) / Zhao, YuXin (author) / Tian, KaiLun (author) / Tang, Bin (author)


    Publication date :

    2025-04-01


    Size :

    2792353 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    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

    Free access

    Time-period Analysis for Pulse Train Deinterleaving

    Nishiguchi, K. | British Library Online Contents | 2004