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- W2022201267 abstract "ABSTRACT Information fusion includes the integration of feature data, expert knowledge. and algorithms. For example, in automatic target recognition (ATR) features of size, color, and motion can be fused to assess the combination of multi-modal information. A neurofuzzy fusion of features captures the multilevel language content of sensory information by fusingneural network data analysis with rule-based decision making. Additionally, the neurofuzzy architecture can effectively fusecoarse and fine abstracted feature data at the content level for decision making. In this paper, we investigate a multilevelneuro-fuzzy feature-based architecture for synthetic aperture radar (SAR) target recognition.Keywords: Sensor Fusion, Target Recognition, Classification, Identification, Neurofuzzv 1. INTRODUCTION Humans and machines interact for specific tasks such as the identification (ID) of a target. When the human looks at atarget, they typical search fur target features such as size, shape. color, form, and motion [1.2]. Cognition, the act of" @default.
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- W2022201267 title "<title>Multilevel feature-based fuzzy fusion for target recognition</title>" @default.
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