Sparse recovery space-time adaptive processing (SR-STAP) can achieve an ideal clutter suppression with very few training samples, however, its application faces two challenges: 1) severe gird mismatch effect and 2) large time-resources requirement. In practice, a coarse space-time grids will bring a serious mismatch between the true clutter points and the divided grids, which leads to a significant performance degradation of clutter suppression. Although the high-resolution mesh can effectively reduce the grid mismatch effect, its cost is huge computational load. Thus, it is meaningful to reduce the large-scale dictionary operation complexity while maintaining suboptimal clutter suppression performance for SR-STAP when applying in real airborne radar system. This article proposed a tensor-based SR-STAP scheme aims at large-scale dictionary application. In the proposed framework, traditional vector-based operations are replaced by their corresponding low-complexity tensor representation. As a result, a large-scale matrix operation can be degraded into multiple small-scale matrix calculation, thus the huge computational loading can be saved in recovery. A comparison of tensor-based SR-STAP and traditional vector-based SR-STAP in large-scale dictionary application is also exhaustive discussed here. Based on this framework, a tensor-based sparse Bayesian learning and its fast matrix-realization form are developed. A series of carefully designed numerical simulation and measurement experiments indicate that the significant advantages of the tensor-based SR-STAP whether in performance or computation loading.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Tensor-Based Sparse Recovery Space-Time Adaptive Processing for Large Size Data Clutter Suppression in Airborne Radar


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


    Erscheinungsdatum :

    01.04.2023


    Format / Umfang :

    6265455 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch