The article presents the cognitive radar architecture of Fraunhofer FHR based on a three-layer model of human cognitive performance. The approach is illustrated using examples for non-cooperative target identification and classification. On the skill based layer, a target-matched waveform design is presented and experimental results are shown. For the transition to the rule-based layer, convolutional neural networks are explained for the identification of air-targets and a novel auto-encoder for change detection is introduced. For rule-based behavior, a policy based decision making algorithm for NCTI waveform selection is explained, using CPOMDPs.


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    Titel :

    Cognitive Radar for Classification


    Beteiligte:
    Bruggenwirth, Stefan (Autor:in) / Warnke, Marcel (Autor:in) / Wagner, Simon (Autor:in) / Barth, Kilian (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2019-12-01


    Format / Umfang :

    3722838 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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