电子学报 ›› 2020, Vol. 48 ›› Issue (4): 734-742.DOI: 10.3969/j.issn.0372-2112.2020.04.015

• 学术论文 • 上一篇    下一篇

微弱信号检测的变尺度Duffing振子方法

行鸿彦1,2, 吴慧1,2, 刘刚1,2   

  1. 1. 南京信息工程大学气象灾害预报预警与评估协同创新中心, 江苏南京 210044;
    2. 南京信息工程大学, 江苏省气象探测与信息处理重点实验室, 江苏南京 210044
  • 收稿日期:2018-10-08 修回日期:2019-04-19 出版日期:2020-04-25 发布日期:2020-04-25
  • 基金资助:
    国家自然科学基金(No.61671248);国家重点研发计划(No.2018YFC1506102);江苏省重点研发计划(No.BE2018719);江苏省"信息与通信工程"优势学科计划

Variable-Scale Duffing Oscillator Method for Weak Signal Detection

XING Hong-yan1,2, WU Hui1,2, LIU Gang1,2   

  1. 1. Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China;
    2. Jiangsu Key Laboratory of Meteorological Detection and Information Processing, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
  • Received:2018-10-08 Revised:2019-04-19 Online:2020-04-25 Published:2020-04-25

摘要: 针对强噪声背景下微弱信号检测问题,本文把互补集总经验模式分解(CEEMD)方法和变尺度Duffing振子结合,提出了一种新的微弱信号检测方法.利用CEEMD将复杂含噪信号分解为不同的固有模态函数(IMF),通过Duffing系统分岔图及其变化找到相轨迹变化的临界阈值,实现含噪信号的信息检测.结果表明,本文所提方法不仅可以很好地免疫噪声,而且能有效检测出信噪比低至-73dB的多频率周期信号.

关键词: 微弱信号检测, 混沌, 分岔图, 互补集总经验模式分解方法

Abstract: Aiming at the weak signal detection problem under strong noise background,the weak signal detection principle based on Duffing oscillator is analyzed.Combining the complementary ensemble empirical mode decomposition(CEEMD)method with the variable-scale Duffing oscillator,a new weak signal detection method is proposed.The complex noisy signal is decomposed into different intrinsic mode functions(IMF) by using CEEMD.Through the Duffing system bifurcation diagram and its changes,the critical threshold of the phase trajectory change is found,and the information detection of the noisy signal is realized.The results show that the joint detection method can not only immune noise well,but also effectively detect multi-frequency periodic signals with signal-to-noise ratio as low as -73dB.

Key words: weak signal detection, chaos, bifurcation diagram, complementary ensemble empirical mode decomposition

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