阈值阵列模型与超阈值随机共振

研究了随机共振的一个特殊形式——超阈值随机共振。超阈值随机共振是基于阈值阵列模型的随机共振现象。分析了阈值阵列模型输出随机过程的统计特性,固定输入信噪比,可观察输出信噪比增益随阈值噪声方差的改变产生非单调变化的规律。实验验证了含噪周期输入信号经阈值阵列系统,在统计独立、服从高斯分布的阈值噪声作用下,输出信号信噪比增益大于1。且对于非高斯噪声,会获得更高的输出信噪比。     

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。     

周期脉冲序列在阈值系统中随机共振的研究

将混有加性高斯白噪声的周期脉冲序列通过阈值系统,对其传输性质进行理论研究.以输入-输出的信号幅度和信噪比增益来分析随机共振现象,通过调节各相关参数使系统、信号和噪声因协同作用而产生随机共振现象:在某一最优噪声强度下获得最大的信号幅度及信噪比的增益.     

离散马尔可夫跳变系统的降维观测器设计

针对一类含有未知输入和测量噪声的线性离散时间马尔可夫跳变系统,设计了一种降维观测器,可以对系统中的未知输入和测量噪声同时解耦.以线性矩阵不等式的形式给出观测器存在的充分条件,并确保误差系统有限时间随机稳定;之后利用一种代数重构的思想实现了未知输入的估计;最后通过数值仿真验证了所提方法的有效性.     

比较阈值电路中噪声对信号传输作用的研究

本文研究了光电探测系统的比较阈值电路中的噪声对信号传输的影响。从理论上计算了该系统的输出自相关函数、功率谱密度和信噪比；分析了比较阈值电路中的随机共振现象；说明对非线性系统可以用噪声来增强信号的传输能力。     

基于三稳态随机共振的BPSK信号检测算法

基于高斯白噪声下BPSK信号接收误码率较高的问题,提出了一种基于三稳态随机共振系统来降低BPSK信号误码率的方法。首先,基于随机共振的BPSK通信系模型,推导了系统的输出信噪比及信噪比增益的表达式。其次,以信噪比增益为衡量指标,探究了系统参数a、b、c和噪声强度D对共振输出的影响。研究表明,随着噪声强度的增加,输出信噪比呈现单峰结构;信噪比增益始终大于1,峰值随着系统参数b的增大而逐渐增大,但随着系统参数a和c的增大而减小。最后,在强噪声背景下,对BPSK信号的传统接收方案和所提方法的误码率进行仿真对比分析,结果证实信号在经过随机共振系统后,时域图像更为清晰,毛刺明显减少,频谱幅值是传统方法的4.613倍。     

比较阈值电路中噪声对信号传输作用的研究

本文研究了光电探测系统的比较阈值电路中的噪声对信号传输的影响。从理论上计算了该系统的输出自相关函数、功率谱密度和信噪比，分析上了比较阈值电路中的随机共振现象，说明对非线性系统可以用噪声来增强信号的传输能力。     

谐振式硅基集成光学陀螺的偏振噪声建模与分析

偏振噪声是谐振式集成光学陀螺的主要光学噪声源,其存在大大降低了系统的精度,为了定量化研究谐振式集成光学陀螺偏振噪声的产生机理,利用琼斯矩阵和光束传播法建立了谐振式硅基集成光学陀螺偏振噪声模型,该模型综合考虑了波导传输介质中的光偏振态交叉耦合、应力双折射等的影响,有效地逼近了实际的物理系统。基于上述模型得出了谐振腔内二氧化硅波导本征偏振态交扰与陀螺极限输出之间的表达式。对波导谐振腔内与偏振相关的3个因素:输入光偏振态、温度波动和波导保偏性能进行了仿真分析。并通过在输入端插入高偏振度起偏器的实验装置,有效验证了所建偏振理论模型受输入光偏振态波动影响的正确性。     

采用主分量分析方法研究随机共振

噪声能量向信号能量的转换是随机共振具有的独特优点,也是实际应用随机共振进行弱信号检测的基础。由于随机共振系统只在合适量的噪声输入时产生最大的信噪比输出(共振),过多、过少的噪声都会降低系统的性能,因此,如何有效地控制输入到随机共振系统中的噪声十分重要。本文首次提出采用主分量分析方法研究随机共振,其基本思路是分解信号和噪声能量到相互正交的主轴方向上,之后进行重构,通过控制重构主轴的数目(重构维数),获得最优量的输入噪声。实验结果证实了方法的有效性。     

基于伪随机序列的微弱光信号检测

 提出了一种测量微弱光信号的方法,用伪随机序列调制光源,调制后的光信号注入测量系统,输出光信号经过光电转换后与伪随机序列进行相关检测,由于伪随机序列具有与白噪声类似的相关特性,测量过程中的干扰和噪声对相关函数峰值影响甚小,相关检测显著提高了输出信号的信噪比,可以检测到微弱的光信号.文中理论分析了伪随机序列相关检测性能,并给出了实验测量结果.理论和实验结果都证明这种方法用于微弱光信号测量系统,在较强噪声背景下(信噪比-15dB),可以提高信噪比约30dB,有效地提高了测量精度.     

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.     

有限字符输入系统的物理层安全传输条件

针对无线数字通信系统中人工噪声方法可被多天线窃听者破解的问题,该文提出有限字符输入下物理层安全传输的一个充分条件,并以此为指导设计了一种类符号人工噪声方法。分析表明,人工噪声方法下,有限字符输入信号和窃听者无噪接收信号之间的等效信道是一个离散有噪无损信道(Discrete Noisy Lossless Channel, DNLC)。由于DNLC输入信号的可逆性为窃密提供了必要条件,窃听者通过增加天线可使窃听信道容量达到合法用户的信道容量上限,致使系统的保密互信息为零,因此破坏输入信号的可逆性是有限字符输入下物理层安全传输的一个充分条件。类符号人工噪声方法满足这一充分条件,可以保证物理层安全传输,仿真结果也表明了该方法的有效性。     

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。     

Semianalytic BER evaluation by simulation for noisy nonlinearbandpass channels

The bit error rate (BER) of channels including a memoryless bandpass nonlinearity is evaluated by simulation. This would typically be an onboard travelling wave tube (TWT) amplifier in a satellite repeater, when the noise at the input of such a nonlinear element is non-negligible. The usual evaluation technique, error counting, requires a large amount of computer time if small error probabilities are to be estimated. It is shown that faster semianalytic procedures can be used, provided that a proper model for the nonlinear element is adopted. The output process is decomposed into a signal component plus an additional term representing an equivalent noise component, and an equivalent nonlinear transformation, relating the input useful signal to the output signal component, is derived. In addition, several modes for the probability density function (PDF) of the uplink noise component at the output of the transmission chain are discussed and compared. The procedure has been tested on a transparent satellite link using 4-CPSK modulation format. The results compare well with those of the error-counting technique if a composite rectangular PDF with exponential tails as adopted     

Statistics of companding noise for QAM transmission

In modern voiceband data communication, the received signal is subject to nonlinear quantization noise due to companding. Under certain conditions, this quantization noise may become dominant and cause serious degradation in performance. In order to design better signal constellations for this environment, it is of interest to characterize this noise. We study the conditional probability density function (PDF) for the quantization noise found on a PCM companding channel. We obtain an analytical expression for the conditional PDF of the nonlinear quantization noise and an algorithm to compute the conditional PDF of the filtered noise, which is the actual noise at the slicer input of the linear receiver. We compare the results to a Gaussian PDF (with second-order moments that depend on the signal point) and conclude that a Gaussian approximation, although not entirely accurate, is not unreasonable     

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。     

低信噪比条件下基于随机共振的感知方法与性能分析

针对认知网络实际环境中常呈现出噪声高动态变化、低信噪比特征,无法快速准确进行频谱感知的问题,本文将物理学非线性领域中的随机共振理论引入到频谱感知中,提出了一种基于广义随机共振的能量检测算法.该算法引入匹配噪声,通过匹配非线性系统、噪声和信号三者的关系,从而改变能量检测统计量的分布,有效地检测信号的存在性.本文从理论上推导了最佳匹配噪声的表达式,并得到了检测性能、受噪声不确定度的影响、感知时间等方面的重要理论结论.仿真结果验证了理论推导的正确性,表明所提算法能够在信噪比为-20dB等低信噪比条件下较现有能量检测算法提高3dB以上,且具有感知速度快、受噪声不确定度影响小等特点.     

Quantization noise in single-loop sigma-delta modulation withsinusoidal inputs

An exact nonlinear difference equation is derived and solved for a simple sigma-delta modulator consisting of a discrete-time integrator and a binary quantizer inside a single feedback loop and an arbitrary input signal. It is shown that the system can be represented as an affine operation (discrete-time integration of a biased input) followed by a memoryless nonlinearity. An extension of the transform method for the analysis of nonlinear systems is applied to obtain formulas for first- and second-order time-average moments of the binary quantization noise, including the sample mean, energy, and autocorrelation. The results are applied to the special case of a sinusoidal input signal to evaluate these time averages and the power spectrum. In the limit of large oversampling ratios, the marginal moments behave as if the quantization noise had a uniform distribution. The spectrum is neither white nor continuous, however, even in the limit of large oversampling ratios