基于随机共振进行弱信号探测的实验研究

非线性随机共振系统可利用噪声增强微弱信号检测的能力,为强噪声背景下微弱信号的检测开创了新方法.基于随机共振的基本原理设计了硬件电路系统,并将其应用于检测单频和多频微弱信号;通过输入模拟工程实际的带噪信号,采样所得的输出信号的频谱分析结果表明,利用随机共振技术可从强噪声背景下有效地提取出单频和多频弱信号.多频弱信号的有效提取拓展了基于随机共振原理的弱信号检测技术的应用领域,结合数字滤波处理技术有效地消除了低频噪声对信号识别的影响.基于随机共振的弱信号检测技术在信息识别与信息处理方面具有巨大的潜在的应用价值.     

调制与解调用于随机共振的微弱周期信号检测

提出了调制随机共振方法，实现了在大参数条件下从强噪声中检测微弱周期信号.将混于噪声中的较高频率的弱信号经调制变为一差频的低频信号作用于随机共振体系，该低频信号满足绝热近似理论，因而能产生随机共振；再经解调可获得埋于噪声中的原较高频率的弱信号.对埋于噪声中的未知频率，可采用连续改变调制振荡器的频率，以获得一个适当的差频信号输入到随机共振体系，根据输出信号共振谱峰的变化经解调而得待检弱信号的未知频率.该方法应具有较高的应用前景. 关键词： 调制与解调 非线性双稳系统 随机共振 微弱信号检测     

α稳定噪声环境下多频微弱信号检测的参数诱导随机共振现象

本文将α稳定噪声与双稳随机共振系统相结合, 研究了不同α稳定噪声环境下高低频(均为多频)微弱信号检测的参数诱导随机共振现象, 探究了α稳定噪声的特征指数α(0 < α ≤ 2)和对称参数β (-1≤ β ≤ 1)及随机共振系统参数a, b对共振输出效应的作用规律. 研究结果表明, 在不同分布的α稳定噪声环境下, 通过调节系统参数a和b均可诱导随机共振来实现多个高、低频微弱信号的检测, 且存在多个a, b参数区间均可诱导随机共振, 这些区间不随α或β的变化而变化; 在高、低频微弱信号检测中, α或β对随机共振输出效应的作用规律相同. 本研究结果将有助于α稳定噪声环境下参数诱导随机共振现象中系统参数的合理选取, 进而可为实现基于随机共振的多频微弱信号检测方法的工程应用奠定基础. 关键词： 随机共振 α稳定噪声')" href="#">α稳定噪声 多频微弱信号检测 平均信噪比增益     

关联噪声驱动的非对称双稳系统的随机共振

运用统一色噪声近似理论和两态模型理论,研究了周期矩形信号和关联的乘性色噪声和加性白噪声驱动的非对称双稳系统的随机共振现象,得到了适合信号任意幅值的信噪比表达式.信噪比是乘性噪声强度、加性噪声强度、乘性噪声自关联时间、噪声耦合强度的非单调函数,所以该双稳系统中出现了随机共振.同时,调节加性噪声强度比调节乘性噪声强度更容易产生随机共振.势阱静态非对称性和噪声之间的耦合强度对信噪比的影响是不同的. 关键词： 非对称双稳系统 随机共振 信噪比 周期矩形信号     

二次采样随机共振频谱研究与应用初探

研究了双稳系统随机共振频谱的洛伦兹分布特征，得出在谱分布能量较集中的低频区才能产生可辨识的随机共振谱峰. 探讨了大参数信号双稳系统的二次采样随机共振的频谱特性. 以强噪声中弱信号的检测为实例，阐述了二次采样随机共振技术的具体应用. 关键词： 随机共振 二次采样随机共振 双稳系统 频谱     

噪声交叉关联强度的时间周期调制对线性过阻尼系统的随机共振的影响

针对由加性、乘性噪声和周期信号共同作用的线性过阻尼系统, 在噪声交叉关联强度受到时间周期调制的情况下,利用随机平均法推导了系统响应的信噪比的解析表达式. 研究发现这类系统比噪声间互不相关或噪声交叉关联强度为常数的线性系统具有更丰富的动力学特性, 系统响应的信噪比随交叉关联调制频率的变化出现周期振荡型随机共振, 噪声的交叉关联参数导致随机共振现象的多样化.噪声交叉关联强度的时间周期调制的引入有利于提高对微弱周期信号检测的灵敏度和实现对周期信号的频率估计. 关键词： 随机共振 周期振荡型共振 噪声交叉关联强度 信噪比     

α稳定噪声驱动的非对称双稳随机共振现象

以微弱周期信号激励的非对称双稳系统为模型,以信噪比增益为指标,首先针对加性和乘性α稳定噪声共同作用的随机共振现象展开了研究,然后针对单独加性α稳定噪声激励的随机共振现象进行了研究,探究了α稳定噪声特征指数α和对称参数β分别取不同值时,系统结构参数a,b,刻画双稳系统非对称性的偏度r以及α稳定噪声强度放大系数Q或D对非对称双稳系统共振输出的作用规律.研究结果表明,无论在加性和乘性α稳定噪声共同作用下还是在单独加性α稳定噪声作用下,通过调节a和b或者r均可诱导随机共振,实现微弱信号的检测,且有多个参数区间与之对应,这些区间不随α或β的变化而变化;在研究噪声诱导的随机共振现象时发现,调节噪声强度放大系数也可使系统产生随机共振现象,且达到共振状态时D的区间也不随α或β的变化而变化.这些结论为α稳定噪声环境下参数诱导随机共振中系统参数以及噪声诱导随机共振中噪声强度的合理选取提供了依据.     

基于分数阶可停振动系统的周期未知微弱信号检测方法

本文建立了分数阶可停振动系统, 其可停振动状态的改变对周期策动力敏感, 对零均值随机微小扰动不敏感, 这事实上为周期未知微弱信号检测提供了一种新的高效检测方法和判别标准. 与现有的利用混沌系统的大尺度周期状态变化检测周期未知弱信号的方法 需逐一尝试设置不同频率内置信号以便期望与待检周期信号发生共振不同, 利用分数阶可停振动系统的可停振动状态变化检测周期未知微弱信号的方法, 除了同样具有因为状态变化对周期信号的敏感性而能够实现极低检测门限的特点外, 还具有混沌系统信号检测所不具有的优点: 1)无需预先估计待检信号的周期; 2)无需计算系统状态的临界阈值; 3)可停振动状态可由本文设计的指数波动函数可靠地进行判断; 4)通过系统微分阶数的变化, 将检测系统层次化, 从而可得到比整数阶检测系统更低的检测门限, 特别是在色噪声环境下, 通过选取合适的微分阶数, 基于分数阶可停振动系统的微弱周期信号检测法能够大幅度的降低检测门限, 在本文的仿真试验中, 检测门限可达-182 dB. 关键词： 分数阶非线性系统 Duffing振子 弱信号检测     

耦合双稳系统的随机共振控制

两个双稳系统经非线性耦合而成为多稳态系统,该耦合系统与单一双稳系统相比具有较高的理论研究和实际应用价值.解析地分析了耦合系统在含噪弱周期信号作用下的响应特性,给出了耦合系数和双稳系统参数对随机共振的影响,表明耦合系统的随机共振是在带状的双势阱作用下产生的,还构建了反馈耦合控制原理框图.这为在双稳类系统中人为地产生随机共振或使共振效应更加强烈即随机共振的控制及其应用提供了可靠的理论依据.数值仿真结果与理论分析完全符合. 关键词： 耦合双稳系统 随机共振 控制     

两种色噪声共存的双稳光学系统中的随机共振现象

当双稳光学系统中包含有一个微弱的周期性信号,且系统中乘性噪声是色噪声,加性噪声是白噪声,而且两种噪声之间的耦合为色噪声并具有不同的噪声相关时间τ1和τ2时,我们研究了两种不同种类的色噪声对随机共振的影响.     

Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction

Stochastic resonance (SR) is an important approach to detect weak vibration signals from heavy background noise. In order to increase the calculation speed and improve the weak feature detection performance, a new bistable model has been built. With this model, an adaptive and fast SR method based on dyadic wavelet transform and least square system parameters solving is proposed in this paper. By adding the second-order differential item into the traditional bistable model, noise utilization can be increased and the quality of SR output signal can be improved. The iteration algorithm for implementing the adaptive SR is given. Compared with the traditional adaptive SR method, this algorithm does not need to set up the searching range and searching step size of the system parameters, but only requires a few iterations. The proposed method, discrete wavelet transform and the traditional adaptive SR method are applied to analyzing simulated vibration signals and extracting the fault feature of a rotor system. The contrastive results verify the superiority of the proposed method, and it can be effectively applied to weak mechanical fault feature extraction.     

Optimum adaptive array stochastic resonance in noisy grayscale image restoration

Considering the widespread noise interference in the two-dimensional (2D) image transmission processing, we proposed an optimal adaptive bistable array stochastic resonance (SR)-based grayscale image restoration enhancement method under low peak signal-to-noise ratio (PSNR) environments. In this method, the Hilbert scanning is adopted to reduce the dimension of the original grayscale image. The 2D image signal is converted into a one-dimensional (1D) binary pulse amplitude modulation (BPAM) signal. Meanwhile, we use the adaptive bistable array SR module to enhance the 1D low SNR BPAM signal. In order to obtain the restored image, we transform the enhanced BPAM signal into a 2D grayscale image signal. Simulation results show that the proposed method significantly outperforms the classical image restoration methods (i.e., mean filter, Wiener filter and median filter) both on the grayscale level and the PSNR of the restored image, particularly in a low PSNR scenario. Larger array size brings better image restoration effect.     

A novel parameter-induced adaptive stochastic resonance system based on composite multi-stable potential model

Stochastic resonance (SR) is used widely as a weak signal detection method by using noise in many fields. In order to improve the weak signal processing capability of SR, a novel composite multi-stable model is proposed, which is constructed by the joint of the tristable model and the Gaussian Potential (GP) model. The SR system based on this model is constructed and the signal-to-noise ratio (SNR) is regarded as the index to measure the SR effect. The differential brain storm optimization (DBSO) algorithm is used to optimize the system parameters collaboratively to achieve parameter-induced adaptive SR. The influences of the system parameters V and R and the noise intensity D on the output response of SR system are analyzed under Gaussian white noise and α stable noise environments, and the advantages of the composite multi-stable SR system over the traditional tristable system are verified. For different levels of weak signals, the output performances of SR systems based on composite multi-stable model, traditional tristable model, composite tristable model are compared and analyzed. The results prove that the proposed model has better performance. Meanwhile, the adaptive detection of the multiple high-frequency weak signal is realized using the composite multi-stable SR system. The simulation results show that the proposed system has strong weak signal processing capability and good immunity to noise types, which widens the application range of SR in practical engineering.     

Stochastic resonance in multi-scale bistable array

This Letter explores a new mechanism of stochastic resonance (SR) that is induced by the multi-scale noise decomposed from the input signal, which is promising in signal detection and processing under heavy background noise. The input signal is firstly decomposed to multi-scale signals by orthogonal wavelet transform. Then, the approximate signal, which contains the driving signal, is processed by an uncoupled parallel bistable array with the detailed signal of each scale as the internal noise. At last, a SR mechanism combining the effects of colored noise and array SR is proposed. The simulation results show that a high quality output signal can be obtained by the new mechanism. The proposed model is more adaptive to input signal with high noise intensity than single bistable SR system, which can be seen from the signal-to-noise ratio curves and average noise intensity curves.     

A weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance

Aiming at detecting the weak signal in a strong noise background, an enhanced weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance is proposed. Firstly, because the system can switch among the monostable, bistable and tri-stable state, the potential function characteristic of tri-stable systems is studied by analyzing the potential function curves with different system parameters. And the dynamic characteristics of system parameters on the depth of the potential well is analyzed. The ranges of R and the system parameters are determined, which is essential for ensuring the system is tri-stable state. Secondly, the range of R is used as the constraint condition and the average output signal-to-noise ratio is used as the fitness function of the adaptive algorithm. The system parameters a, b, c are optimized by the differential evolution particle swarm optimization (DEPSO) method to obtain the best output effect. Finally, the proposed adaptive parameter-induced tri-stable stochastic resonance method is adopted to detect the mixed multiple high-frequency weak signal. The detection results are compared with that of adaptive bistable stochastic resonance. At the meanwhile, the method is also applied to detect the fault signal of single crystal furnace. Both the simulation analysis and experiment results show that the proposed method can effectively improve the output signal-to-noise ratio and detect multi-frequency weak signal in the strong noise background.     

基于自适应随机共振理论的太赫兹雷达信号检测方法

太赫兹雷达系统在差频信号频谱分析过程中,干扰噪声影响其测距能力.针对上述问题,提出基于自适应随机共振理论的太赫兹雷达信号检测方法,通过对含噪差频信号进行二次采样,利用自适应随机共振系统提取信号,进行尺度恢复完成测距计算.实验数据显示,不同测量距离时,相较于快速傅里叶变换法,输出信噪比的平均增益为9.684 d B,其中测量距离为1000 mm处,差频信号初始频谱值提高了64.1倍,系统信噪比增益为11.761 d B;相较于滤波法,在测量距离为1000 mm处信噪比增益最大,提高了70.56%;输入噪声强度为1—5 V之间时,输出信噪比曲线的曲率相对于滤波法降低了86.5%,其中噪声强度为5 V时信噪比增益最大,为14.018 d B.实验表明太赫兹雷达系统的测距能力大幅提高.     

Improving the Stochastic Resonance in a Bistable System with the Bounded Noise Excitation

Journal of Statistical Physics - The stochastic resonance (SR) phenomenon is investigated in a typical bistable system that excited by the bounded noise and the weak low-frequency signal...     

级联双稳系统的随机共振特性

研究了两个双稳系统级联的随机共振特性，由于第一级双稳系统的作用是将白噪声转变为色噪声，因此它是整个级联系统中最重要的环节，以后各级系统近似按洛伦兹分布将噪声能量不断向低频区域集中，从而减弱高频抖动，突出波形的基本轮廓.频谱中信号谱峰随噪声强度的变化规律表明，级联双稳系统只在有限的低频范围内，通过一定量的噪声强度来增强信号频率处的谱峰高度，如果前一级系统未达到随机共振状态，那么其后一级并不能对前一级的输出进行“优化”而形成随机共振.级联双稳系统级数的增加，会使噪声能量集中的低频区域变窄，信号谱峰易被压缩和受到噪声干扰.虽然可以用二次采样方法进行改善，但其改善程度有限.因此对于信号检测而言，使用单级双稳系统即可. 关键词： 级联双稳系统 随机共振 频谱 噪声     

A new method for weak fault feature extraction based on piecewise mixed stochastic resonance

In a continuous bistable system, when the input signal is continuously increased, the output signal tends to be stable and no longer increases. At this time, the weak signal under strong background noise is difficult to be extracted, which means saturation occurs. Aiming at the saturation characteristics of stochastic resonance (SR), the proposed piecewise nonlinear bistable system (PNBSR) model has achieved certain results. However, the potential barrier in the middle of the PNBSR method still completely uses the potential function of classical bistable stochastic resonance (CBSR). There is no fundamental solution to the fourth-order limitation. This paper explores an improved piecewise mixed stochastic resonance (PMSR) potential model. The fourth-order potential function that restricts particle motion in CBSR is improved to a piecewise second-order potential function. This potential function subverts the shape of the traditional potential function and presents a symmetrical double-hook shape. Based on PMSR model, this paper uses particle swarm optimization (PSO) to select system parameters and elaborates the characteristics of the potential function curve in detail. Under the same conditions, the output signal-to-noise ratio (SNR) curve of the improved system is generally higher than that of the CBSR and PNBSR systems. Experiments on bearings and gears show that the proposed method can accurately extract weak fault features, and the effect is better than the PNBSR method.