摘要
提出了一种提取强混沌中微弱谐波信号的方法。该方法根据嵌入定理,利用混沌系统的单变量观测值对混沌背景重构相空间,并利用径向基函数神经网络(RBFNN)建立混沌噪声的一步预测模型,使其与混沌噪声具有相同的基本动力学特征。并结合一个梳状滤波器对预测误差进行滤波,从而检测出湮没在混沌中的感兴趣的微弱谐波信号。该方法在信噪比(SNR)为-46dB时仍可检测出强混沌中微弱谐波信号。
This paper presents a neural network method to detect weak harmonic signal embedded in chaotic 憂oise? Based on embedding theorem, the method utilizes the observed values of single variable of chaotic system to reconstruct phase space. The radial basis function neural network(RBFNN)is used to build one-step predictive model which has the same underlying dynamics as the chaotic background. Combining with a comb-filter, predictive error is processed so that weak harmonic signal is extracted from strong chaotic noise. The method can detect weak harmonic signal when the signal-noise-radio (SNR) gets-46dB.
出处
《通信学报》
EI
CSCD
北大核心
2004年第5期75-82,共8页
Journal on Communications
基金
国家自然科学基金资助项目(40374045)
吉林省科技发展计划基金资助项目(20020626)
关键词
微弱信号检测
混沌
神经网络
重构相空间
weak signal detection
chaos
neural network
reconstruct phase space
