For multichannel airborne radars, wide-area ground-moving target indication (WGMTI) processing can quickly obtain the dynamic distribution of moving targets in a wide area, which holds considerable significance in many fields. Nevertheless, the WGMTI mode suffers from the interference of powerful ground clutter, which frequently submerges slow-moving targets and causes many false alarms in subsequent moving target detection. Space–time adaptive processing (STAP) can successfully suppress clutter, but its performance depends critically on the available training samples. Consequently, an effective STAP method characterized by fast processing and a small sample size for WGMTI applications in multichannel airborne radars must be developed. In this article, a subarray-level sparse recovery STAP processing framework is proposed for multichannel airborne radars. First, the characteristics of the subarray-level received clutter are discussed in detail. Second, on the basis of this analysis, we further designed a joint space–time dictionary and developed a separable tensor-based sparse Bayesian learning (STSBL) method. In this method, two-stage decomposition is proposed to ensure that large-scale data can be degraded into small-scale data in processing, which significantly improves computation efficiency. Finally, the effectiveness of the proposed STSBL-STAP method in WGMTI processing was verified using real measurement data obtained from a developed dual-channel Ku-band airborne radar.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Reduced-Complexity Subarray-Level Sparse Recovery STAP for Multichannel Airborne Radar WGMTI Application


    Beteiligte:
    Cui, Ning (Autor:in) / Xing, Kun (Autor:in) / Yu, Zhongjun (Autor:in) / Duan, Keqing (Autor:in)


    Erscheinungsdatum :

    01.10.2023


    Format / Umfang :

    35600087 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Clutter Suppression for STAP-Based Radar Using Synthesized Subarray Beampattern

    Wan, Fuhai / Xu, Jingwei / Xu, Yanhong et al. | IEEE | 2025


    Reduced Dimension STAP Based on Sparse Recovery in Heterogeneous Clutter Environments

    Zhang, Wei / An, Ruixue / He, Ningyu et al. | IEEE | 2020


    Reduced-rank STAP for high PRF radar

    Ayoub, T.F. / Haimovich, A.M. / Pugh, M.L. | IEEE | 1999