Specific emitter identification based on intrinsic time-scale-decomposition and image texture feature

Abstract

Specific emitter identification (SEI) aims at discerning radio emitters using external measurement features called the radio frequency (RF) fingerprints of the signal. SEI is widely used in intelligent gathering, network intrusion detection, Cognitive Radio and so on. In this paper, we proposed a novel SEI method based on steady signals, which is more practical. We firstly get the time-frequency-energy distribution (TFED) of the signal using Intrinsic Time-scale Decomposition(ITD), which is more suitable for non-stationary signal than empirical mode decomposition (EMD). Secondly, we transform the TFED of the signal into a gray image and several image texture features are extracted from histogram statistics and Gray-Level Co-Occurrence Matrix (GLCM) of the image. We treat these texture features as the signal's RF fingerprints to identify the specific emitter. Measured ship signals and simulated signals are used to evaluate the performance of the proposed method and another two methods are compared with it. Experimental results demonstrate that the proposed method is more effective.