This paper presents an explainable late-stage decision fusion model for Electro-Optical (EO) and Passive Radio Frequency (P-RF) target detection via hybrid Explainable AI model. Explainable insights that are intuitive and empirical are provided by counterfactual explanations at the early stage of data flow, with a traditional algorithm, decision tree (DT), handling late-stage fusion. Results show that at both the local and global level, the DT explainability of fusion methods provides insights for EO and P-RF fusion methods at each level of fusion The usage of Histograms, Wigner-Ville Distribution (WVD) and Continuous Wavelet Transform (CWT) for the novel use of P-RF data provided insights into the eXplainable Late-stage Fusion of Electro-optical and Radio-Frequency (xLFER) usage of the modality for target detection. While WVD and CWT have been used extensively in RF signal processing, their use in P-RF data for target detection feature extraction has not been documented to our knowledge, nor with a hybrid Explainable AI model.


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    Titel :

    Explainable Hybrid Decision Level Fusion for Heterogenous EO and Passive RF Fusion via xLFER


    Beteiligte:
    Vakil, Asad (Autor:in) / Blasch, Erik (Autor:in) / Ewing, Robert (Autor:in) / Li, Jia (Autor:in)


    Erscheinungsdatum :

    28.08.2023


    Format / Umfang :

    1182383 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch