Long time coherent integration (LTCI) for high-speed maneuvering targets in radar signal processing poses substantial difficulties due to the occurrence of range migration (RM) and Doppler frequency migration (DFM). The generalized Radon–Fourier transform (GRFT), an effective method for accumulating target energy, is hampered by its high computational cost. To address this, the full-dimensional partial-search generalized Radon–Fourier transform (FSGRFT), a fast implementation of the GRFT algorithm, is proposed in this article. Differing from the exhaustive full-dimensional search of the GRFT algorithm, the FSGRFT algorithm first employs a pretrained residual network to coarse estimate range cells where targets might exist, along with their corresponding motion parameter subspaces. Guided by this initial coarse estimate, the FSGRFT algorithm narrows its search to a subset of range cells and the designated subspace of motion parameters, thereby considerably decreasing the number of searches. Meanwhile, the introduction of a lightweight network structure with robust representation capabilities ensures the accuracy of the coarse estimation, without bringing excessive computational cost. Therefore, the FSGRFT algorithm obtains a good tradeoff between the computational cost and integration performance, in comparison with other LTCI algorithms. Finally, simulation results demonstrate that the FSGRFT algorithm can achieve detection performance comparable to the GRFT algorithm, but with considerably lower computational cost.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Full-Dimensional Partial-Search Generalized Radon–Fourier Transform for High-Speed Maneuvering Target Detection


    Beteiligte:
    Jiang, Wenqi (Autor:in) / Liu, Hongwei (Autor:in) / Jiu, Bo (Autor:in) / Zhao, Yu (Autor:in) / Li, Kang (Autor:in) / Zhang, Yu (Autor:in)


    Erscheinungsdatum :

    2024-08-01


    Format / Umfang :

    7920026 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch