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Electromagnetic Object Recognition for Dielectric Coated Conductors Based on WD-PCA Type Fused Feature Extraction

Sayan, Gönül
Ergin, E.
This paper presents the application of a real-time electromagnetic object classification technique to recognize dielectric coated conducting objects using their wideband scattered signals received at arbitrary combinations of aspect and polarization. The suggested classifier design technique depends on the use of Singularity Expansion Method (SEM) to represent a given electromagnetic scatterer by its natural resonances. A quadratic time-frequency transformation, the Wigner distribution (WD), is used to extract pole-related feature vectors from scattered signals, and the Principal Components Analysis (PCA) is further used to obtain a fused feature vector for each object of concer