基于自适应随机共振高频微弱信号检测
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摘要
为了实现对微弱大信号的检测,提出了一种基于参数归一化方法和人工鱼群算法自适应随机共振微弱大信号检测的方法。通过对大信号的归一化处理,使得大信号满足了产生随机共振条件,将随机共振输出信号的信噪比作为目标函数,利用人工鱼群算法的实现了对信号最优输出的自适应。仿真结果表明,归一化随机共振结合了人工鱼群算法,能够实现最优信噪比的自适应输出,且是一种适应于任意频率下信号检测方法。
In order to realize the detection of weak signals, an adaptive stochastic resonance weak signal detection method based on normalization method and artificial fish swarm algorithm is proposed. The normalizing signal can satisfie the requires of stochastic resonance. The output signal-to-noise ratio gain of stochastic resonance system is used as fitness function of the artificial fish swarm algorithm. The simulation by MATLAB show that a single-tone signal after the normalized stochastic resonance is combined with the intelligent algorithm, the output signal-to-noise ratio is improved compared to the normalized stochastic resonance. But the stability of this method has some room of improvement.
In order to realize the detection of weak signals, an adaptive stochastic resonance weak signal detection method based on normalization method and artificial fish swarm algorithm is proposed. The normalizing signal can satisfie the requires of stochastic resonance. The output signal-to-noise ratio gain of stochastic resonance system is used as fitness function of the artificial fish swarm algorithm. The simulation by MATLAB show that a single-tone signal after the normalized stochastic resonance is combined with the intelligent algorithm, the output signal-to-noise ratio is improved compared to the normalized stochastic resonance. But the stability of this method has some room of improvement.
引文
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[4] GAMMAITONI L,MARCHEONI F,MENICHELLASAETTA E and SANTUCCI S.Stochastic resonance in bistable systems[J/OL].Phys.Rev.Lett.1989,62(4):349-352[2018-06-27].http://dx.doi.org/10.1103%2FPhysRevLett.62.349.
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[6] 杨定新,胡政,杨拥民.大参数周期信号随机共振解析[J/OL].物理学报,2012,61:(8):080501-080503[2018-06-27].http://dx.doi.org/10.7498/aps.61.080501.
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[9] 冷永刚基于Kramers逃逸率的调参随机共振机理[J/OL].物理学报,2009,58(8):5198 [2018-06-27].http://pdf.d.cnki.net/cjfdsearch/pdfdownloadnew.asp?encode=gb&nettype=cnet&zt=A00.DOI:10.3321/j.issn:1000-3290.2009.08.011.
[10] 李晓磊,邵之江,钱积新一种基于动物自治体的寻优模式:鱼群算法[J/OL].系统工程理论实践,2002,22(11):32-38 [2018-06-27].http://www.cnki.com.cn/Article/CJFDTotal-XTLL200211006.htm.DOI:10.3321/j.issn:1000-6788.2002.11.007.
[11] 行鸿彦,卢春霞,张强.自适应随机共振微弱信号检测[J/OL].系统仿真学报,2018,30(2):587-604[2018-06-27].http://www.cnki.com.cn/Article/CJFDTotal-XTFZ201802028.htm.DOI:10.16182/j.issn1004731x.joss.201802027.
[12] KOHAR V,MURALI K,SINHA S.Enhanced logical stochastic resonance under periodic forcing[J/OL].Communications in Nonlinear Science and Numerical Simulation.2014,19 (8):2866-2873 [2018-06-27].http://dx.doi.org/10.1016/j.cnsns.2013.12.008.
[2] GAMMAITONI L,HANGGI P,JUNG P,et.Stochastic Resonance:A remarkable idea that changed our perception of noise[J/OL].European Physical Journal B (S1434-6028),2009,69(1):1-3 [2018-06-27].http://dx.doi.org/10.1140/epjb/e2009-00163-x.
[3] 卢志恒,林建恒,胡岗.随机共振问题Fokker-Planck方程的数值研究[J/OL].物理学报,1993(10):1556-1566[2018-06-27].http://dx.doi.org/10.7498/aps.42.1556.
[4] GAMMAITONI L,MARCHEONI F,MENICHELLASAETTA E and SANTUCCI S.Stochastic resonance in bistable systems[J/OL].Phys.Rev.Lett.1989,62(4):349-352[2018-06-27].http://dx.doi.org/10.1103%2FPhysRevLett.62.349.
[5] 冷永刚,王太勇.二次采样用于随机共振从强噪声中提取弱信号的数值研究.物理学报,2003,52(10):2432-2437 [2018-06-27].http://dx.doi.org/10.3321/j.issn:1000-3290.2003.10.014.
[6] 杨定新,胡政,杨拥民.大参数周期信号随机共振解析[J/OL].物理学报,2012,61:(8):080501-080503[2018-06-27].http://dx.doi.org/10.7498/aps.61.080501.
[7] ZHANG H,XIONG W,ZHANG S B,HE Q B.Nonstationary weak signal detection based on normalization stochastic resonance with varying parameters.Sadhana - Academy Proceedings in Engineering Sciences [J/OL],2016,41(6):621-632 [2018-06-27].https://link.springer.com/article/10.1007/s12046-016-0503-x.
[8] CHU Z Y,LIN M,HUANG Y.Weak signal detection method with adaptive coupled bistable system based on Genetic Algorithm[C/OL]//IEEE.2016 Sixth International Conference on Instrumentation & Measurement,Computer,Communication and Control.IEEE,2016:830-833[2018-06-27] https://doi.org/10.1109/IMCCC.2016.116.
[9] 冷永刚基于Kramers逃逸率的调参随机共振机理[J/OL].物理学报,2009,58(8):5198 [2018-06-27].http://pdf.d.cnki.net/cjfdsearch/pdfdownloadnew.asp?encode=gb&nettype=cnet&zt=A00.DOI:10.3321/j.issn:1000-3290.2009.08.011.
[10] 李晓磊,邵之江,钱积新一种基于动物自治体的寻优模式:鱼群算法[J/OL].系统工程理论实践,2002,22(11):32-38 [2018-06-27].http://www.cnki.com.cn/Article/CJFDTotal-XTLL200211006.htm.DOI:10.3321/j.issn:1000-6788.2002.11.007.
[11] 行鸿彦,卢春霞,张强.自适应随机共振微弱信号检测[J/OL].系统仿真学报,2018,30(2):587-604[2018-06-27].http://www.cnki.com.cn/Article/CJFDTotal-XTFZ201802028.htm.DOI:10.16182/j.issn1004731x.joss.201802027.
[12] KOHAR V,MURALI K,SINHA S.Enhanced logical stochastic resonance under periodic forcing[J/OL].Communications in Nonlinear Science and Numerical Simulation.2014,19 (8):2866-2873 [2018-06-27].http://dx.doi.org/10.1016/j.cnsns.2013.12.008.