Sparsity-based multipath exploitation is a promising method to eliminate ghost targets in through-the-wall radar images and utilize the additional energy in secondary reflections. The applicability of existing methods, however, is limited due to the assumption of perfectly known geometry of building interiors. We develop a parametrized multipath signal model that captures unknown or partially known wall locations. This model is used in the proposed joint image reconstruction and wall position estimation method. In order to further improve practicability in realistic scenarios, a reconstruction method based on deployment of multiple small aperture radar modules is discussed. To this end, we analyze theoretical performance bounds for colocated and distributed placements of the various modules. Supporting results based on simulated and experimental lab data are provided.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Parametric dictionary learning for sparsity-based TWRI in multipath environments


    Beteiligte:


    Erscheinungsdatum :

    01.04.2016


    Format / Umfang :

    935944 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Parametric dictionary learning for sparsity-based TWRI in multipath environments

    Leigsnering, Michael / Ahmad, Fauzia / Amin, Moeness G. et al. | IEEE | 2016


    Dictionary Learning-Structured Reinforcement Learning With Adaptive-Sparsity Regularizer

    Li, Zhenni / Tang, Jianhao / Zhao, Haoli et al. | IEEE | 2024


    Jitter diversity in multipath environments

    Noprklit,O. / Eggers,P.C. / Nadersen J.B. et al. | Kraftfahrwesen | 1995



    An Imaging Dictionary Based Multipath Suppression Algorithm for Through-Wall Radar Imaging

    Shisheng Guo / Guolong Cui / Lingjiang Kong et al. | IEEE | 2018