Electromagnetic target classification using time-frequency analysis and neural networks

Abstract

This paper demonstrates the feasibility and advantages of using a self-organizing map (SOM)-type neural network classifier for electromagnetic target recognition. The classifier is supported by a novel feature extraction unit in which the Wigner distribution (WD), a time-frequency representation, is utilized for the extraction of natural-resonance-related energy feature vectors from scattered fields. The proposed target classification technique is tested for a set of canonical targets, displaying an excellent performance in terms of both real-time classification speed and accuracy, even in the presence of noisy data.