Recently, the passive bistatic radar (PBR) that exploits frequency modulation (FM) radio transmitters as illuminators, has witnessed widespread usage owing to its various advantages. However, the characteristics of FM-radio-based PBR result in interference components in the range–Doppler (RD) map, which may increase false alarms. Therefore, this study proposes a method for suppressing interference components using a deep learning approach. The two main contributions of this study are as follows. First, a convolutional autoencoder model capable of effectively suppressing interference in the RD map of the PBR was proposed. Second, a synthetic RD map dataset generation method that can enable the autoencoder to operate robustly in PBR in a real environment was presented. Further, a performance comparison between the proposed method and existing methods using simulated data proved that the deep learning-based method exhibited superior target detection performance. Furthermore, using the data recorded by the PBR in a real environment, the proposed autoencoder model was shown to effectively suppress interference components in a real interference environment.


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    Title :

    Interference Suppression for an FM-Radio-Based Passive Radar via Deep Convolutional Autoencoder


    Contributors:


    Publication date :

    2024-02-01


    Size :

    2931938 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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