Specific Emitter Identification Based on Joint Wavelet Packet Analysis
Zhenhan Zhao, Jian Chen, Wenbo Xu, Hanyang Li, Long Xiang Yang · 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN) · 2022
Specific emitter identification (SEI) has been recognized as a powerful technique for civil and military communications to distinguish the radio emitters. In particular, the key idea of this technique is analyzing the received signals to extract the intrinsic non-linearities incurred by hardware impairments of radio emitters, in order to classify those radio emitters that the signals originated from. As the modern electromagnetic environment becomes ultra complicated, the signal propagation may experience severe co-channel interference due to the spectrum reuse. However, many previous SEI methods cannot work properly in low signal-to-noise (SNR) regime, thereby leading to significant recognition rate loss. To address this problem, this paper proposes a novel SEI algorithm using joint wavelet packet analysis. The algorithm decomposes the signal using wavelet packet decomposition, and analyzes the joint wavelet packet coefficient matrix to reduce the influence of noise on the effective information. By extracting features of singular value center of gravity, instantaneous frequency distribution and information demission, the specific emitter is identified by support vector machine (SVM) based on voting mechanism. The simulation results show the effectiveness of using we proposed algorithm to extract features under low SNR environment. The recognition rate of the proposed algorithm is over 83% when the SNR is OdB and the proposed algorithm achieves more than 25% improvement over the traditional wavelet analysis algorithm at 0~5dB.