The unfolding network, which combines traditional compressive sensing methods and deep learning, has been widely used in sparse aperture (SA) inverse synthetic aperture radar (ISAR) imaging because of its good interpretability, high reconstruction accuracy, and computational efficiency. However, most unfolding networks view SA ISAR imaging under different sampling matrices as diverse problems, resulting in poor network transferability when the sampling matrix changes. In this article, a complex-valued residual alternating direction method of multipliers-Net (CV-Res-ADMM-Net) is designed to address this issue. Based on the radar signal's complex-valued (CV) characteristics, we designed a residual learning-based CV convolutional neural network that includes three modules: feature extraction, nonlinear mapping, and feature accumulation to implement the nonlinear operation to enhance the network's ISAR image feature extraction capability. The residual learning technique can accelerate the training process and smooth network information transmission. Moreover, a multisampling matrix generation approach is developed, which can improve the training diversity and maintain more structural information on ISAR images for more satisfactory reconstruction. Simulated and real-measured experiments verify that CV-Res-ADMM-Net has good transferability when the sampling matrix changes. Experimental results show the proposal can achieve fast SA ISAR imaging of maneuvering targets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    CV-Res-ADMM-Net for Sparse Aperture ISAR Imaging


    Beteiligte:
    Huang, Quan (Autor:in) / Zhang, Lei (Autor:in) / Wei, Shaopeng (Autor:in) / Duan, Jia (Autor:in)


    Erscheinungsdatum :

    01.08.2025


    Format / Umfang :

    4356106 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Parametric sparse representation method for ISAR imaging of rotating targets

    Wei Rao / Gang Li / Xiqin Wang et al. | IEEE | 2014



    Resolution enhanced sparse aperture imaging

    Miller, N. / Duncan, B. / Dierking, M.P. | IEEE | 2006


    Origins of sparse aperture imaging

    Pauls, T.A. | IEEE | 2001