To overcome the limitations of LiDAR and camera systems in cost and weather-resilient performance, millimeter-wave radar has emerged as a complementary solution for object identification and surveillance. Previous studies have indicated promising prospects for frequency-modulated continuous wave radar in action recognition tasks, primarily through classifying microDoppler features in the radar's slow-time dimension. However, these features are highly sensitive to the direction of target motion, causing significant variations when observed from different angles. To address these challenges while enhancing cost-efficiency and environmental robustness, we introduce a stacked dual-path feature fusion network (SDP-Net). The design of the model is validated using an unsupervised image clustering algorithm. We also present lightweight variants of SDP-Net to ensure broad hardware compatibility and demonstrate robust omnidirectional motion recognition using monostatic radar. Comparative evaluation on both simulated and measured datasets confirms that our approach outperforms existing recognition methods.


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    Titel :

    A Low-Cost Dual-Path Feature Fusion Network for Omnidirectional Human Motion Recognition Using Monostatic Radar


    Beteiligte:
    Qiu, Ye (Autor:in) / Li, Xinglong (Autor:in) / Deng, Zhenmiao (Autor:in) / Huang, Xiaohong (Autor:in) / Pan, Pingping (Autor:in) / Ma, Xinjie (Autor:in)


    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    6891477 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch