科技创新与应用2025，Vol.15Issue(7)：13-17,22,6.DOI:10.19981/j.CN23-1581/G3.2025.07.004
基于流形学习与稀疏描述方法的辐射源个体指纹识别技术
李成 1谢阳 2李德峰 1蔡玉宝 1曹亮3
作者信息
- 1. 中国电子科技集团公司第二十七研究所,郑州 450047
- 2. 中国人民解放军 96637部队,北京 102100
- 3. 海装某代表室,郑州 450006
- 折叠
摘要
Abstract
Based on the nonlinear model of the power amplifier,individual fingerprint features are extracted from the signal bispectrum,and the dimension of fingerprint features is reduced with the help of the two-dimensional supervised decision-preserving projection algorithm in manifold learning.Then,using Euclidean distance and description error as sparse criteria,two matching classification algorithms are proposed,K-nearest neighbor sparse description method and K-nearest neighbor feature space method to identify and classify dimensional-reduced fingerprints.Simulations are used to verify the effectiveness of manifold learning and sparse description in individual fingerprint identification.The results show that compared with the global description classification method,the two proposed algorithms have better recognition performance;compared with individual recognition algorithms based on Hilbert-Huang transform and approximate entropy,the proposed algorithms avoid parameter selection problems and are more robust,suitable for complex electromagnetic environments.关键词
辐射源个体/指纹识别/非线性模型/流形学习/稀疏描述Key words
individual emitter/fingerprint identification/nonlinear model/manifold learning/sparse description分类
电子信息工程引用本文复制引用
李成,谢阳,李德峰,蔡玉宝,曹亮..基于流形学习与稀疏描述方法的辐射源个体指纹识别技术[J].科技创新与应用,2025,15(7):13-17,22,6.
