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.
Explainable Hybrid Decision Level Fusion for Heterogenous EO and Passive RF Fusion via xLFER
28.08.2023
1182383 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Optimal Multiple Level Decision Fusion with Distributed Sensors
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