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Natural resonance-based feature extraction with reduced aspect sensitivity for electromagnetic target classification

This paper presents a model-based electromagnetic feature extraction technique that makes use of time–frequency analysis to extract natural resonance-related target features from scattered signals. In this technique, the discrete auto-Wigner distribution of a given signal is processed to obtain a partitioned energy density vector with a significantly reduced sensitivity to aspect angle. Each partition of this vector contains, in the approximate sense, spectral distribution of the signal energy confined to a particular subinterval of time. Selection of sufficiently late-time partitions provides target features with a markedly increased target discrimination capacity. The potential of the suggested technique and the practical issues in its implementation are demonstrated by applying it to realistic target classification problems with very encouraging results.