levy噪声背景下级联系统中弱信号的提取
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摘要
针对冲击噪声背景下弱周期信号难以提取的问题,提出了以levy噪声作为背景噪声的级联随机共振方法;首先,在数值上分析了随机共振系统最佳参数区间与levy噪声参数的关系;其次,总结了系统输出的微弱信号频谱值跟随系统参数的变化规律;最后,利用级联系统对levy噪声背景下微弱信号的提取进行了研究;实验结果表明,随机共振参数的最佳区间不随噪声参数α、β的变化而变化;系统输出信号的频谱幅值会随噪声参数α、β的改变而改变,但浮动不大;在级联系统中,二级系统输出的待检测信号频谱值是一级系统的2.2倍;该系统对冲击环境中弱信号的提取具有很强的实用性。
Aiming at the difficulty of extracting weak periodic signals in the background of impact noise,the cascade stochastic resonance method based on levy noise as background noise is proposed.Firstly,the relationship between the spectrum value of weak signal and the levy noise parameters is summarized numerically.Secondly,the changes of the parameters of the system parameters are analyzed.Finally,the cascade system is used to extract the weak signal under the background of levy noise.The results show that the optimal interval of the random resonance parameters don't change with the change of the noise parameterαandβ,The amplitude of the system output signal will change with the change of noise parameterαandβ,but not much.In cascade system,the spectrum value of the output signal to be detected by the second level system is 2.2 times that of the first level system.The system has strong practicability for the extraction of weak signal in the impact environment.
Aiming at the difficulty of extracting weak periodic signals in the background of impact noise,the cascade stochastic resonance method based on levy noise as background noise is proposed.Firstly,the relationship between the spectrum value of weak signal and the levy noise parameters is summarized numerically.Secondly,the changes of the parameters of the system parameters are analyzed.Finally,the cascade system is used to extract the weak signal under the background of levy noise.The results show that the optimal interval of the random resonance parameters don't change with the change of the noise parameterαandβ,The amplitude of the system output signal will change with the change of noise parameterαandβ,but not much.In cascade system,the spectrum value of the output signal to be detected by the second level system is 2.2 times that of the first level system.The system has strong practicability for the extraction of weak signal in the impact environment.
引文
[1]Benzi R,Srutera A,Vulpiani A.The mechanism of stochastic resonance[J].J.Phys A.,1981,V4(11):453-457.
[2]Fauve S,Heslot F.Stochastic Resonance in a Bistable System[J].Phys.Rev.Lett,1983,97A:5-7.
[3]冷永刚,赖志慧,范胜波,等.二维Duffing振子的大参数随机共振及微弱信号检测研究[J].物理学报,2012,61(23):230502-230502.
[4]王志霞,郭利.改进PSO算法调参的随机共振微弱信号检测[J].计算机测量与控制,2018(1):42-46.
[5]Wang Z,Qiao Z,Zhou L,et al.Array-enhanced Logical Stochastic Resonance Subject to Colored Noise[J].Chinese Journal of Physics,2017,55(2):252-259.
[6]Lefebvre J,Hutt A,Frohlich F.Stochastic resonance mediates the state-dependent effect of periodic stimulation on cortical alpha oscillations[J].Elife,2017,6(e32054):1:21.
[7]Mcdonnell M D,Stocks N G,Abbott D.Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance.[J].Physical Review E Statistical Nonlinear&Soft Matter Physics,2007,75(1):061105.
[8]宁丽娟,徐伟.光学双稳系统中的随机共振[J].物理学报,2007,56(4):1944-1947.
[9]兀旦晖,杨萍.随机共振技术在弱随机二元码检测中的应用研究[J].计算机测量与控制,2010,18(4):770-772.
[10]尚金红,王辅忠,张光璐,等.基于随机共振的2PSK信号相干接收误码率的研究[J].应用声学,2015,34(6):495-500.
[11]Gao Y X,Wang F Z.Adaptive cascaded-bistable stochastic resonance system research and design[J].Computational and Theoretical Nanoscience,2013,10:1-5.
[12]Shi P,An S,Li P,et al.Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method[J].Measurement,2016,90:318-328.
[13]Xie Y,Liu R N.Stochastic resonance in overdamped washboard potential system[J].Acta Physica Sinica,2017,66(12):120501.
[14]王立国,谢寿生,胡金海,等.基于随机共振的航空发动机转子多域融合神经网络故障诊断[J].计算机测量与控制,2013,21(6):1483-1486.
[15]Weron R.On the Chambers-Mallows-Stuck method for simulating skewed stable random variables[J].Statistics&Probability Letters,1996,28(2):165-171.
[16]焦尚彬,孙迪,刘丁,等.α稳定噪声下一类周期势系统的振动共振[J].物理学报,2017,66(10):100501.
[17]Wan P,Zhan Y J,Li X C,et al.Numerical reasearch of signal-to-noise gain on a monostable stochastic resonance[J].Journal of Physics,2011,60(4):53-59.
[2]Fauve S,Heslot F.Stochastic Resonance in a Bistable System[J].Phys.Rev.Lett,1983,97A:5-7.
[3]冷永刚,赖志慧,范胜波,等.二维Duffing振子的大参数随机共振及微弱信号检测研究[J].物理学报,2012,61(23):230502-230502.
[4]王志霞,郭利.改进PSO算法调参的随机共振微弱信号检测[J].计算机测量与控制,2018(1):42-46.
[5]Wang Z,Qiao Z,Zhou L,et al.Array-enhanced Logical Stochastic Resonance Subject to Colored Noise[J].Chinese Journal of Physics,2017,55(2):252-259.
[6]Lefebvre J,Hutt A,Frohlich F.Stochastic resonance mediates the state-dependent effect of periodic stimulation on cortical alpha oscillations[J].Elife,2017,6(e32054):1:21.
[7]Mcdonnell M D,Stocks N G,Abbott D.Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance.[J].Physical Review E Statistical Nonlinear&Soft Matter Physics,2007,75(1):061105.
[8]宁丽娟,徐伟.光学双稳系统中的随机共振[J].物理学报,2007,56(4):1944-1947.
[9]兀旦晖,杨萍.随机共振技术在弱随机二元码检测中的应用研究[J].计算机测量与控制,2010,18(4):770-772.
[10]尚金红,王辅忠,张光璐,等.基于随机共振的2PSK信号相干接收误码率的研究[J].应用声学,2015,34(6):495-500.
[11]Gao Y X,Wang F Z.Adaptive cascaded-bistable stochastic resonance system research and design[J].Computational and Theoretical Nanoscience,2013,10:1-5.
[12]Shi P,An S,Li P,et al.Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method[J].Measurement,2016,90:318-328.
[13]Xie Y,Liu R N.Stochastic resonance in overdamped washboard potential system[J].Acta Physica Sinica,2017,66(12):120501.
[14]王立国,谢寿生,胡金海,等.基于随机共振的航空发动机转子多域融合神经网络故障诊断[J].计算机测量与控制,2013,21(6):1483-1486.
[15]Weron R.On the Chambers-Mallows-Stuck method for simulating skewed stable random variables[J].Statistics&Probability Letters,1996,28(2):165-171.
[16]焦尚彬,孙迪,刘丁,等.α稳定噪声下一类周期势系统的振动共振[J].物理学报,2017,66(10):100501.
[17]Wan P,Zhan Y J,Li X C,et al.Numerical reasearch of signal-to-noise gain on a monostable stochastic resonance[J].Journal of Physics,2011,60(4):53-59.