Abstract:

For communication emitter identification,a novel method based on Hilbert-Huang transform (HHT) and multi-scale fractal features was proposed.First,the time frequency energy spectrum was derived via HHT,which was called a complicated curved surface in the three-dimension space,namely,3D-Hilbert energy spectrum.Then,the differential box dimension and the multi-fractal dimension was extracted to compose the feature vector under multi-scale segmentation using fractal theory.Finally,communication emitter individual identification was obtained using the two dimensions of features above and the support vector machine (SVM).Moreover,the novel method was compared with two existing methods to identify simulated and actual signals with different and the same modulation modes,respectively.Results show that the identification rate of the novel method is higher than that of the two other methods.The features extracted by the novel method have high stability,sufficiency,and identifiability,also outweigh the negative effects of the change of signal-to-noise ratio and the number of training samples and emitters.

Key words: specific emitter identification, 3D-Hilbert energy spectrum, multi-scale, differential box dimension