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

Abstract

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.

Specific Emitter Identification via Bispectrum-Radon Transform and Hybrid Deep Model

“…us, extraction of unintentional modulation features from signals makes it possible to identify specific emitters. Various methods have been proposed to characterize UMOP [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. ese methods fall into categories of waveform-, transform-, and transmitterbased approaches.…”

“…ey also perform poorly when analyzing non-Gaussian and nonstationary processes. For these reasons, researchers have proposed other transforms to extract features in the transform domain, including the ambiguity function [6], the wavelet transform [7], the Hilbert-Huang transform (HHT) [8][9][10], and short-time Fourier transform [11,12]. ese transforms are appropriate for analyzing nonlinear and nonstationary signals.…”

Frequency‐Hopping Signal Network‐Station Sorting Based on Maxout Network Model and Generative Method

“…Based on the different types of RF fingerprint feature extraction, current feature extraction method of FH signal can be classified into two main categories: transient feature extraction and steady-state feature extraction [8][9][10]. Considering the fact that when a FH station is activated or "keyed," it goes through a relatively short transient phase during which the FH signal emanating from the unit displays characteristics that are believed to be unique to the extent that they can be used to unambiguously identify an individual FH station.…”

“…Considering the fact that when a FH station is activated or "keyed," it goes through a relatively short transient phase during which the FH signal emanating from the unit displays characteristics that are believed to be unique to the extent that they can be used to unambiguously identify an individual FH station. Individual feature analysis of steady-state signals is more difficult than transient signal feature extraction [10]. Under steady-state operation, the internal device differences of the station are expressed in the form of "synthesis."…”

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

“…High-order cumulants [7], wavelet ridge and high order spectra [8], bispectrum and its variants [9][10][11] have demonstrated the effectiveness for the given conditions. Recently, Hilbert-Huang transform (HHT) has proven the superiority in the unique representation and descriptive ability for SEI [12][13][14][15]. It provides an accurate amplitude distribution with the change of time and frequency, but does not need the prior information about the analyzed signal.…”