Specific emitter identification based on Hilbert–Huang transform‐based time–frequency–energy distribution features

Abstract

A novel specific emitter identification method based on transient communication signal's time–frequency–energy distribution obtained by Hilbert–Huang transform (HHT) is proposed. The transient starting point is detected using the phase-based method and the transient endpoint is detected using a self-adaptive threshold based on the HHT-based energy trajectory. Thirteen features that represent both overall and subtle transient characteristics are proposed to form a radio frequency (RF) fingerprint. The principal component analysis method is used to reduce the dimension of the feature vector and a support vector machine is used for classification. A signal acquisition system is designed to capture the signals from eight mobile phones to test the performance of the proposed method. Experimental results demonstrate that the method is effective and the proposed RF fingerprint can represent more subtle characteristics than the RF fingerprints based on instantaneous amplitude, phase, frequency and energy envelope. This method can be equally applicable for any wireless emitter to enhance the security of the wireless networks.