信息工程大学学报2023，Vol.24Issue(5)：544-551,8.DOI:10.3969/j.issn.1671-0673.2023.05.006
基于HHT与正则化维数的辐射源个体识别
Specific Emitter Identification Based on Hilbert-Huang Transform and Regularization Dimension
摘要
Abstract
The feature extraction of signal is the key step of specific emitter identification(SEI).To extract more discriminating features of communication emitters,a novel approach based on Hilbert-Huang transform(HHT)and regularization dimension(RD)is proposed.Firstly,a signal was decom-posed into multiple intrinsic mode function components in the time domain by empirical mode de-composition algorithm to obtain time-frequency energy spectrum and marginal spectrum.Then,RD of time-frequency energy spectrum and marginal spectrum were calculated respectively to characterize complexity of the signal,and energy entropy was combined to form a feature vector.Finally,support vector machine classifier was utilized to identify different emitters.Experimental result shows that RD has good intra-class aggregation and inter-class separability,and the proposed approach outperforms the other two classical approaches based on HHT energy spectrum especially under low SNR condi-tions.关键词
辐射源个体识别/希尔伯特-黄变换/正则化维数Key words
specific emitter identification/Hilbert-Huang transform/regularization dimension分类
信息技术与安全科学引用本文复制引用
惠周勃,刘伟,王世举,王艳云..基于HHT与正则化维数的辐射源个体识别[J].信息工程大学学报,2023,24(5):544-551,8.
