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色噪聲背景下微弱周期脈沖信號(hào)的混沌檢測(cè)方法

李月,  楊寶俊,  杜立志,  袁野

李月, 楊寶俊, 杜立志, 袁野. 色噪聲背景下微弱周期脈沖信號(hào)的混沌檢測(cè)方法[J]. 電子與信息學(xué)報(bào), 2003, 25(2): 195-199.
引用本文: 李月, 楊寶俊, 杜立志, 袁野. 色噪聲背景下微弱周期脈沖信號(hào)的混沌檢測(cè)方法[J]. 電子與信息學(xué)報(bào), 2003, 25(2): 195-199.
Li Yue, Yang Baojun, Du Lizhi, Yuan Ye. Chaos-Based weak periodic pulse signal detection approach under colored noise background[J]. Journal of Electronics & Information Technology, 2003, 25(2): 195-199.
Citation: Li Yue, Yang Baojun, Du Lizhi, Yuan Ye. Chaos-Based weak periodic pulse signal detection approach under colored noise background[J]. Journal of Electronics & Information Technology, 2003, 25(2): 195-199.

色噪聲背景下微弱周期脈沖信號(hào)的混沌檢測(cè)方法

Chaos-Based weak periodic pulse signal detection approach under colored noise background

  • 摘要: 該文將微弱周期脈沖信號(hào)引入特定的混沌系統(tǒng)中，以系統(tǒng)相軌跡由混沌狀態(tài)到大尺度周期狀態(tài)的躍遷作為微弱周期脈沖信號(hào)的檢測(cè)依據(jù)。用特定的混沌系統(tǒng)實(shí)現(xiàn)了對(duì)強(qiáng)噪聲背景下周期脈沖信號(hào)的檢測(cè)，仿真實(shí)驗(yàn)表明該混沌檢測(cè)系統(tǒng)對(duì)被強(qiáng)噪聲覆蓋的周期脈沖信號(hào)非常敏感，對(duì)任何色噪聲均具有極強(qiáng)的抑制能力。
    Abstract: This paper introduces weak periodic pulse signal into given chaotic system, and uses the change of system phase track which leaps from chaotic status to periodic status as the detection criterion. The given chaotic system is used to realize weak periodic pulse signal detection under strong noise background. Simulation experiments show that chaos detection system is very sensitive to periodic pulse signal contained by strong noise and has strong constraint to any colored noise.
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  • 戴逸松,微弱信號(hào)檢測(cè)方法及儀器,北京,國(guó)防工業(yè)出版社,1994,268-275.[2]B.D. Rao, K. V. S. Hari, Performance analysis of root-MUSIC, IEEE Trans. on Acoustics,Speech, Signal Processing, 1989, 37(8), 1789-1794.[3]R. Roy, A. Parlraj, T. Kailath, ESPRIT-a subspace rotation approach to estimation of parameters of sinusoid in noises. IEEE Trans. on Acoustics, Speech, Signal Processing, 1986, 34(5), 1340-1342.[4]X.D. Zhang, Y. C. Liang, Prefilter-based ESPRIT for estimating parameters of sinusoids in non-Gaussian noise, IEEE Trans. on Signal Processing, 1995, 45(1), 349-353.[5]A. Swami, J. M. Mendei, Cumulant-based approach to the harmonic retrieval problem, Proc.ICASSP-88[C], New York, 1988, 2264-2267.[6]A. Ferri, G. Alengrin, Estimation of the frequencies of a complex sinusoidal noisy signal using fourth order statistics, Proc. ICASSP-91[C], Toronto, Canada, 1991, 3457-3460 .[7]石要武,戴逸松,丁宏,有色噪聲背景下正弦信號(hào)頻率估計(jì)的互譜Pisarenko和MUSIC方法,電子學(xué)報(bào),1996,24(10), 46-51.[8]R. Roy, T. Kailath, ESPRIT-estimation of signal parameters via rotational invariance techniques,IEEE Trans. on Acoustics, Speech, Signal Processing, 1989, 37(7), 984-995.[9]G. S Xu, Y. H. Pao, Single vector approach to eigen-structure analysis for harmonic retrieval,Proc. ICASSP[C], New York, 1986, 521-524.[10]李月,楊寶俊,石要武等,用混沌振子檢測(cè)淹沒(méi)在強(qiáng)噪聲中的方波信號(hào),吉林大學(xué)自然科學(xué)學(xué)報(bào),2001,136,65-68.[11][意]G.桑森,R康蒂著,黃啟昌等譯,非線性微分方程,北京,科學(xué)出版社,1983,470-480. ［12］B. Roorda, C. Hejj, Global total least squares modeling of multivariable time series, IEEE Trans.on Automatic Control, 1995, 40(1), 50-63.
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出版歷程
  • 收稿日期:  2002-03-29
  • 修回日期:  2002-06-28
  • 刊出日期:  2003-02-19

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