吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (4): 1182-1190.doi: 10.13229/j.cnki.jdxbgxb201404043

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消散型同步的微弱周期信号检测及噪声影响分析

行鸿彦, 龚平, 徐伟   

  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, 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, 南京 210044;
    3.南京信息工程大学 电子与信息工程学院, 南京 210044
  • 收稿日期:2013-01-14 出版日期:2014-07-01 发布日期:2014-07-01
  • 作者简介:行鸿彦(1962-), 男, 教授, 博士.研究方向:微弱信号检测与处理, 仪器仪表设计. E-mail:xinghy@nuist.edu.cn
  • 基金资助:
    国家自然科学基金项目(61072133); 江苏省产学研联合创新计划项目(BY2013-02, BY2011112); 江苏省“信息与通信工程”优势学科建设项目

Weak periodic signal detection based on dissipative coupling synchronization and noise impact analysis

XING Hong-yan1, 2, 3, GONG Ping1, 2, 3, XU Wei1, 2, 3   

  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, University of Information Science and Technology, Nanjing 210044, China;
    2.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China;
    3.College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • Received:2013-01-14 Online:2014-07-01 Published:2014-07-01

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摘要： 在混沌预测模型基础上, 提出了消散型同步的混沌背景下微弱信号检测算法。采用径向基函数神经网络(RBFNN)拟合混沌模型, 结合消散型同步实现混沌时间序列与混沌系统的同步, 利用同步误差实现微弱信号的检测。以Rossler混沌系统为研究对象, 验证了算法的可行性, 研究了噪声对微弱信号检测的影响。仿真研究表明, 该算法能检测各种频率的微弱信号, 在一定条件下可检测到信杂比大于-110 dB的微弱周期信号;若信噪比SNR≥0 dB, 噪声对微弱信号检测的影响很小;但若SNR<-10 dB, 将检测不出微弱信号。在理论研究基础上, 由MKS-CEC-Ⅲ新型混沌演化控制实验仪获取Coullet混沌时间序列, 添加不同频率的微弱信号, 利用该算法实现了不同频率微弱信号的检测, 说明该算法适用于其他混沌系统。

关键词: 通信技术, 混沌, 消散型混沌同步, 微弱信号检测, 径向基函数神经网络

Abstract: Based on the chaos forecasting model, a dissipative coupling detection method is proposed for detecting weak signal in chaotic background. The Radial Basis Function Neural Network (RBFNN) is applied to fit the chaos forecasting model. The synchronization between chaotic time series and chaos system is realized by combination of RBFNN and dissipative coupling. Then the synchronization error is used to detect the weak signal. The Rossler chaos system is taken as the object to test the feasibility of the proposed method and analyze its performance with weak signals of different frequencies. In order to avoid the mixed impact of noise and chaos time series to the performance of the method, the interference of noise intensity on weak signal detection is investigated in depth. Simulation results show that the proposed method can detect weak signal with different frequencies; under certain conditions, the method can detect weak periodic signal if the Signal-to-Clutter Ratio is bigger than -110 dB. The noise influence on the detection performance can be ignored if the Signal-to-Noise Ratio (SNR) ≥ 0 dB. On the basis of theoretical study, a new type chaotic evolution control experiment instrument MKS-CEC-Ⅲ is applied to produce practical Coullet chaotic time series and add weak signals with different frequencies into time series respectively. This method is used to detect the weak signal from the mixed signal. It is demonstrated that the method can detect added frequency signal from chaotic background and it can also be applied to other chaotic systems.

Key words: communication, chaos, dissipative coupling chaos synchronization, weak signal detection, radial basis function neural network

中图分类号: 

  • TN911.23
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