Real time electromagnetic target classification using a novel feature extraction technique with PCA-based fusion

This work introduces an efficient technique to design an electromagnetic target classifier whose reference database is constructed using scattered data at only a few aspects. The suggested technique combines a natural-resonance related feature extraction process with a novel, multiaspect feature fusion scheme. First, moderately aspect-variant late-time features are extracted from scattered field of a given candidate target at several different reference aspects using the Wigner transformation to characterize the target's scattered energy distribution over a selected late-time segment of the joint time-frequency plane. Then, these features are fused using the principal component analysis to obtain a single characteristic feature vector that can effectively represent the target of concern over a broad range of aspect angles. The suggested technique is demonstrated to design a classifier that is verified to be highly accurate and robust even in the presence of excessive noise. Due to the computational efficiency of the technique, the classifier needs very small memory space to store the reference information and quite fast lending itself suitable for real-time target classification.