Communication emitter individual identification via 3D‐Hilbert energy spectrum‐based multiscale segmentation features

Abstract

Summary Specific emitter identification can detect emitters automatically by extracting and analyzing features. A novel specific emitter identification method based on 3D‐Hilbert energy spectrum‐based multiscale segmentation (3D‐HESMS) is proposed. First, the time‐frequency energy spectrum is derived via the Hilbert‐Huang transform, that is, a complicated curved surface in a 3D space, namely, the 3D‐Hilbert energy spectrum. The differential box dimension, multifractal dimension, lacunarity change rate, and 3D‐Hilbert energy entropy are extracted to compose the feature vector under multiscale segmentation using fractal theory. Subsequently, communication emitter individual identification is obtained using the 4 features. Finally, the performance and complexity of the 3D‐HESMS method are compared with those of 2 existing methods. Experiments show that the performance of the 3D‐HESMS method is better than those of the 2 other methods. The extracted features with high stability, sufficiency, and identifiability can overcome the negative effects of the changes in signal‐to‐noise ratio and the number of training samples.