基于随机共振的微弱信号检测

介绍随机共振(SR)方法的基本思想,并建立数学模型。随机共振系统是非线性双稳态系统,存在某一最佳输入噪声强度,使系统产生最高信噪比输出,达到抑制噪声,放大微弱信号的目的。针对传统系统随机共振只适用于极低频信号的局限,本文引入尺度变换,消除了对待检信号频率的限制,通过Matlab仿真,验证了其对微弱信号检测的有效性。     

基于随机共振的微弱信号检测研究

微弱信号是淹没在噪声中的小信号,且一般其信噪比比较低。微弱信号的检测在物理、电子和生物医学方面都具有重要的意义。依据随机共振理论,噪声在一定的条件下有利于微弱信号的检测。研究了随机共振的原理、双稳态系统中的随机共振现象及随机共振的应用研究现状。     

基于随机共振电路模拟的微弱周期信号检测

采用电路模拟非线性Duffing振子，利用其随机共振机制来检测微弱周期信号。针对随机共振只适用于极低频输入信号的限制，引入一种适当的变量变换可以将高频信号转化成符合随机共振理论要求的低频信号进行处理，增强了该方法在工程应用中的可行性。采用电路模拟方法检测微弱周期信号，不需要象随机共振数值仿真所要求的那样对信号过采样，在满足采样定理的条件下，可以取较小的采样频率，降低了对硬件的要求。实验表明，该方法能有效地从强背景噪声中检测出微弱周期信号，在机械系统故障早期检测、化学谱信号提取、多传感器测量等领域有实际应用价值。     

基于级联随机共振的窄带信号宽带化接收

为提高传统窄带接收机的解调增益,提出了基于级联随机共振的窄带信号宽带化接收方法。该方法一方面对接收信号进行随机共振运算,将信号中的噪声转化为信号能量;另一方面通过级联方式将接收信号转化为方波信号,并利用其先验信息构造本地序列进行宽带化相关运算,最终提高了信号处理增益。理论分析与仿真结果表明,该方法相比于传统的处理方式,其信噪比增益提高大于3 dB,并且对于接收信噪比为?7~1 dB的QPSK信号,其增益提高约为20 dB。     

基于分段线性随机共振模型的信号检测电路研究

提出了一种新的分段线性随机共振模型,建立了模型的数学关系,通过强噪声背景下微弱周期信号检测的数值仿真,验证了模型的有效性.根据该模型设计了硬件电路,对不同噪声强度背景下微弱周期信号以及无噪声周期信号进行了随机共振实验研究和对比分析,结果表明,该电路可以实现对强噪声背景下的微弱周期信号检测,并能显著增强输出信噪比.     

Matching pursuit and atomic signal models based on recursive filterbanks

The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictionary of time-frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal adaptive. In this paper, we apply matching pursuit to the derivation of signal expansions based on damped sinusoids. It is shown that expansions in terms of complex damped sinusoids can be efficiently derived using simple recursive filter banks. We discuss a subspace extension of the pursuit algorithm that provides a framework for deriving real-valued expansions of real signals based on such complex atoms. Furthermore, we consider symmetric and asymmetric two-sided atoms constructed from underlying one-sided damped sinusoids. The primary concern is the application of this approach to the modeling of signals with transient behavior such as music; it is shown that time-frequency atoms based on damped sinusoids are more suitable for representing transients than symmetric Gabor atoms. The resulting atomic models are useful for signal coding and analysis modification synthesis     

基于欠采样随机共振的单频微弱信号检测新方法

由于随机共振具有在特定条件下增强微弱信号信噪比的特性,近年来成为一种全新的微弱信号检测手段。为了克服随机共振绝热近似理论小参数条件的限制,提出一种基于欠采样随机共振的微弱信号检测方法。通过欠采样尺度变换与还原技术,实现了大参数信号的随机共振处理,突破了二次采样变尺度随机共振算法要求采样频率必须大于信号频率的50倍的限制。构建了基于欠采样随机共振的微弱信号检测模型,从理论上证明了方法的可行性。最后利用该方法对信噪比为-27 dB条件下的微弱单频信号检测进行了仿真,结果进一步验证了所提微弱信号检测方案的正确性。所提方法大大降低了信号的采样速率,为将随机共振应用于科斯塔斯(Costas)环的改进奠定了基础。     

Multi-frequency weak signal detection based on multi-segment cascaded stochastic resonance for rolling bearings

For rotating machinery, vibration signals excited by its faulty components provide rich condition information for its fault diagnosis and condition-based maintenance. However, strong noise severely influences the accurate detection of incipient faults. Thanks to the ability of enhancing weak input and suppressing the noise, the stochastic resonance (SR) has been applied to weak signal detection in some fields, and the improvement on its performance are still being concerned, especially in the mechanical engineering. For multi-frequency weak signals, this paper proposes an improved mechanism for the SR, called multi-segment cascaded stochastic resonance (MS-CSR). In this method, the input signal obtains segment enhancement by using some bistable SR models, and series connection of such a unit compose an improved cascaded SR (CSR) system, which can not only gradually enhance the weak signals of interest, but also pay more attention on the signal with relatively small amplitude at the initial. A modified measurement index, named alliance signal-to-noise ratio (ASNR) is defined to evaluate the detection performance of the proposed SR method, as well as the parameter selection for the MS-CSR system. In this index, a weight factor is introduced to influence the assignment of noise energy in the SR, so that the relatively weak signal in the multi-frequency input signal can obtain a high priority to make the resonance phenomenon happen and avoid the misdiagnosis. A simulated signal and an experimental vibration signal collected from a faulty bearing are used to verify the effectiveness of the proposed MS-CSR method. The results demonstrate that the MS-CSR is a useful tool for detecting weak signals with multiple characteristic frequencies.     

基于阈上随机共振的弱信号双色中波红外图像融合

提出了基于阈上随机共振的弱信号双色中波红外图像融合方法.利用阈上随机共振分别增强两幅图像的弱信号,通过中值滤波对图像降噪,然后对降噪图像进行支持度变换融合,得到信息丰富、细节明显的融合图像.实验结果表明,融合图像的局部标准差提高了247.7％、局部熵提高了45.52％、峰值信噪比分别提高了6.71％和6.65％,证明了...     

利用随机共振进行弱信号检测的Labview虚拟实验系统

基于Labview虚拟仪器开发系统,建立了利用随机共振检测弱信号的模拟仿真实验系统.通过适当的调节输入信号的振幅、噪声强度和系统参数,可以在模拟仿真实验系统中实时地观察到非线性双稳态系统中发生的随机共振现象,进而检测系统输入的淹没于强噪声背景中的弱信号.该模拟仿真实验系统还可以改造成为检测实际弱信号的探测系统.虚拟仿真实验结果表明基于Labview的随机共振检测弱信号模拟仿真实验系统具有形象直观、实时性强、参数调节简洁等特点.     

Quality assessment models for initial production process control based on stochastic differential equations

It is of great importance to assess an initial production process prior to the regular mass-production. For this purpose, many statistical methods have been proposed for practical use. In this paper, we propose two stochastic models for an assessment method of the initial production process control: a Markov process model and a Markov approximation model. These models are continuous state space models and formulated by applying mathematical techniques of stochastic differential equations. Based on each model, we derive several quantitative assessment measures for initial production process control.     

随机共振瓦斯微弱信号检测方法研究

为了解决检测煤矿复杂环境中的瓦斯信号时易受周围噪声干扰以至微弱信号被掩埋或产生异常数据的问题,提出一种基于随机共振的微弱瓦斯信号检测方法。采用欠采样原理对大频率信号尺度变换及粒子群算法优化系统结构参量,对大参量微弱信号在随机共振系统中的共振效果进行了理论分析和研究。结果表明,该方法可以以较低的采样频率,自适应地达到较好的共振效果;可有效地滤除噪声并增强系统辨识微弱信号的灵敏度以及信号检测的动态范围。该研究为瓦斯突出信息的早期辨识提供了一定的理论依据。     

相位编码信号码速率估计的最优小波尺度选择

研究了小波变换应用于相位编码信号码速率估计的尺度选择问题,提出了一种最优小波尺度选择方法。以相位跳变的最佳检测为依据建立了最优小波尺度选择函数,然后结合功率谱平滑载频估计和信号编码形式完成最优小波尺度的选取,最后通过叠加模值频谱完成信号码速率估计。仿真实验表明,所提出的尺度选择方法将二相编码信号和四相编码信号的码速率估计信噪比门限分别降低了1 dB和3 dB。     

Efficient entropy estimation based on doubly stochastic models for quantized wavelet image data.

Under a rate constraint, wavelet-based image coding involves strategic discarding of information such that the remaining data can be described with a given amount of rate. In a practical coding system, this task requires knowledge of the relationship between quantization step size and compressed rate for each group of wavelet coefficients, the R-Q curve. A common approach to this problem is to fit each subband with a scalar probability distribution and compute entropy estimates based on the model. This approach is not effective at rates below 1.0 bits-per-pixel because the distributions of quantized data do not reflect the dependencies in coefficient magnitudes. These dependencies can be addressed with doubly stochastic models, which have been previously proposed to characterize more localized behavior, though there are tradeoffs between storage, computation time, and accuracy. Using a doubly stochastic generalized Gaussian model, it is demonstrated that the relationship between step size and rate is accurately described by a low degree polynomial in the logarithm of the step size. Based on this observation, an entropy estimation scheme is presented which offers an excellent tradeoff between speed and accuracy; after a simple data-gathering step, estimates are computed instantaneously by evaluating a single polynomial for each group of wavelet coefficients quantized with the same step size. These estimates are on average within 3% of a desired target rate for several of state-of-the-art coders.     

Some results on the stochastic signal parameter estimation problem

The problem of finding maximum-likelihood estimates of the partially or completely unknown autocorrelation function of a zero-mean Gaussian stochastic signal corrupted by additive, white Gaussian noise is analyzed. It is shown that these estimates can be found by maximizing the output of a Wiener estimator-correlator receiver biased by a smoothed version of the output noise-to-signal ratio of the Wiener estimator over the class of admissible autocorrelation functions. For the case where the autocorrelation function is known except for an amplitude scale parameter, an illuminating expression for the Cramer-Rao minimum estimation variance is derived. Detailed study of this expression yields, among other results, an interpretation of the maximum-likelihood estimator as an adaptive processor.     

随机谐振在信号接收中的应用研究

在一些非线性系统中,噪声时常有助于信号传输或信号处理的现象称为随机谐振。本文基于信号错误接收概率讨论随机谐振在信号接收中的应用。对于极大阈值网络,当信号在阈下时,适量的噪声能改善信号接收,随机谐振现象存在;随着网络中阈值单元数的增加,错误接收概率的最小值迅速地趋于零,随机谐振功效极大地提高;对于两种典型噪声解析地讨论发生随机谐振现象时的最佳噪声强度,并根据噪声密度函数的变化对随机谐振机理进行了尝试性解释。     

M-estimation in exponential signal models

In this paper, we propose an M-estimation of the parameters in an undamped exponential signal model. Its asymptotic performance is investigated. Under mild assumptions, the estimation is consistent. The simulation studies of the performance of the M-estimation using Huber's function are provided when the sample size is small, and the comparisons between the performances of the M-estimation and the least squares estimation are also presented     

Optimal adaptive decorrelator for signal separation

The authors show that an optimal length filter can be designed by using a time-delay estimation technique and that its convergence behaviour can be greatly improved. This scheme has significant advantages in real-time implementation and the results clearly demonstrate the improved convergence     

Error bounds for stochastic estimation of signal parameters

This paper is concerned with stochastic-approximation algorithms for estimating signal parameters. Emphasis will be on the performance of the algorithm for a finite number of observations as opposed to the asymptotic convergence rate. We use as an upper bound a result due to Dvoretzky. A lower bound on the average mean-square error is derived. This new bound is based on the Cramér-Rao inequality. The conventional Cramér-Rao bound is not directly applicable, because it requires the knowledge of the bias function, which is difficult to find in a recursive estimation scheme. To avoid this difficulty, we introduce the concept of most favorable bias function and use the calculus of variations to derive the lower bound. The new bound also serves as a standard to judge the merits of the stochastic-estimation algorithm, since under some general conditions no estimate can yield smaller error. It is shown that under some conditions the two bounds are nearly equal, and hence the algorithm is near optimal. The asymptotic efficiency of the algorithm is compared with Sakrison's result. A stochastic-estimation algorithm is derived for estimating Doppler frequency, and performance curves in terms of the error bounds are presented.     

基于矢量信号发生器的微波着陆信号模拟

针对当前微波着陆装备测试的迫切需要,提出一种基于矢量信号发生器硬件架构的微波着陆模拟器的实现方式,并提出了一种基于FPGA的微波着陆系统基带信号设计方法。对微波着陆信号的格式和信号时序进行了仿真,并对各种角功能信号和数据字结构进行了实际测试,证明了该模拟器性能达到装备测试要求。