Radar emitter signal recognition based on entropy features
Gexiang Zhang · Chinese Journal of Radio Science · 2005
To solve the problems of low recognition rate and noise effect in radar emitter signal recognition, a novel approach was proposed. In this approach, approximate entropy (ApEn) and norm entropy (NoEn) constituted feature vector, and neural network based classifiers were designed to identify radar emitter signals automatically. ApEn is a good measure of complexity and irregularity of signals and NoEn is a useful parameter for quantifying the energy distribution of signals. Theoretical analysis and experimental results show that ApEn and NoEn features have small within-class distance and large between-class distance, and can achieve very satisfying accurate recognition rate when signal-to-noise rate varies in a large range. It is proved to be a valid and practical approach.