混沌信号的压缩感知去噪

对非线性时间序列进行噪声抑制是从中提取有效信息的前提. 混沌信号的去噪算法不仅要使滤波后的信号具有较高的信噪比, 也要具有较好的不确定性. 从压缩感知的角度出发,提出了一种新的噪声抑制方法. 该方法包括估计噪声方差, 以及依据动态的稀疏度将观测值往确定的过完备字典上投影. 仿真实验表明, 该方法比常用的小波阈值法和局部曲线拟合法具有更高的输出信噪比, 而原始信号的混沌特性也能得到较大程度的恢复.     

距离无关的高精度混沌激光测距

研究了利用类噪声混沌激光的相关特性实现目标距离的测定方法.数值分析了混沌激光自相关曲线的半峰全宽和旁瓣水平与表征其混沌状态的最大李雅普诺夫指数及关联维数的关系.同时,模拟研究了波形误差对混沌激光测距的影响,并分析了不同混沌态的波形误差的临界值.结果表明,具有窄半峰全宽和低旁瓣水平的自相关曲线的高维混沌激光序列是理想的测距激光,其测量分辨率可达1.5 cm以下,且波形误差容忍度大,测量范围大.在测量范围内,精度与距离无关.     

基于广义窗函数和最小二乘支持向量机的混沌背景下微弱信号检测

为了从混沌背景中检测微弱信号,研究分析了复杂非线性系统的相空间重构理论,提出了一种基于广义窗函数的最小二乘支持向量机的预测法. 该方法以广义嵌入窗为基础,利用自关联函数法确定Lorenz系统的嵌入维数和时间延迟, 实现相空间重构,结合最小二乘支持向量机建立Lorenz系统的误差预测模型, 检测微弱目标信号(瞬态和周期信号).仿真实验表明,该方法的预测模型具有较小的误差, 能够有效地从混沌背景噪声中检测出微弱目标信号,减小噪声对目标信号的影响. 与传统方法相比,在降低检测门限的同时,能够有效地提高预测的精度, 在混沌噪声下信噪比为-87.41 dB的情况下,相对于传统支持向量机方法所得的均方根误差0.049(-54.60 dB时)降低近两个数量级至0.000036123(-87.41 dB时).     

毫米级高分辨率的混沌激光分布式光纤测温技术

近年来,随着分布式光纤传感技术在各大基础设施健康监测领域的广泛应用,人们对能够实现毫米量级精准定位和监测技术的需求日益增长.本文提出了一种基于宽线宽混沌激光的高分辨率分布式光纤测温技术.实验通过改变光反馈混沌源的偏振匹配态和反馈强度等外部参数,产生了–3 dB线宽约为7.5 GHz的宽线宽混沌激光,并在300 m传感光纤实现了空间分辨率为7.05 mm的分布式温度测量.同时,为了抑制光源线宽增加造成的布里渊增益谱恶化,在泵浦路中引入了时间门控技术,其中经脉冲调制后的泵浦光峰值功率提高了约9.5 dB,同时脉冲调制使混沌互相关锁定于脉冲持续时间内,从而布里渊增益谱的信号背景噪声比由约2.28 dB提升为4.55 dB,最终实现了空间分辨率为3.12 mm的分布式温度测量.     

基于Gabor变换的TDLAS检测信号的降噪研究

可调谐二极管激光吸收光谱(TDLAS)技术结合波长调制光谱(WMS)技术是用于痕量气体检测的重要技术手段。通过锁相放大器进行谐波检测,对解调得到的二次谐波信号进行分析可获得气体吸收的信息。但由于二次谐波信号受到噪声的影响,降低了检测系统的精度和稳定性。为了提高TDLAS检测系统的信噪比(SNR),提出了一种基于Gabor变换对二次谐波信号进行数字滤波降噪的方法。以CH₄在1 653.72 nm处的吸收光谱为例,通过仿真和实验对该降噪方法的有效性进行了验证。仿真结果表明,通过Gabor变换对信噪比为0dB的二次谐波信号进行处理后,系统的信噪比可提高15.73 dB。实验结果表明,基于Gabor变换进行降噪处理后,CH₄浓度在0.001%～0.02%区间内与二次谐波峰值的线性相关系数r达到了0.996 59,且系统的检测精度和稳定性明显提高。     

一种混沌海杂波背景下的微弱信号检测方法

基于复杂非线性系统的相空间重构理论,提出了一种基于遗传算法的支持向量机预测方法.利用改进的自相关法和饱和关联维数法确定混沌信号的时间延迟和嵌入维,从而实现相空间重构.通过遗传算法优化支持向量机中的惩罚系数和核函数参数,并结合支持向量机建立混沌序列的单步预测模型,从预测误差中检测出淹没在混沌背景中的微弱信号(包括瞬态信号和周期信号).以Lorenz系统和加拿大McMaster大学利用IPIX雷达实测得到的海杂波数据作为混沌背景噪声进行仿真实验,结果表明该方法能够有效地从混沌背景噪声中检测出微弱目标信号,所得的均方根误差为0.00049521(信噪比为-89.7704 dB),这比传统支持向量机方法的均方根误差(0.049,信噪比为-54.60 dB)降低了两个数量级.     

金属互连电迁移噪声的相关维数研究

针对金属铝互连中噪声信号随电迁移过程变化规律及其所反映的内部失效机理问题，提出将相关维数用于对电迁移噪声时间序列的分析.通过对互连电迁移噪声实验数据的相关维数计算，发现随着电迁移的进行，金属铝互连噪声由随机性成分占主导变为确定性成分占主导，反映出噪声由随机信号转变为混沌动力学信号.应用散射理论解释上述现象，在金属互连电迁移中，空位扩散阶段噪声主要产生机制是空位随机散射；在空位聚集到空洞成核这一过程中，噪声产生机制逐渐从随机散射转变到弹道混沌腔输运机制为主.通过与传统表征参量的对比，证明相关维数可用于预测金 关键词： 电迁移 噪声 相关维数 混沌     

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     

Instability control in microwave-frequency microplasma

This paper introduces comprehensive large-signal analyses of modulation dynamics and noise of a chaotic semiconductor laser. The chaos is induced by operating the laser under optical feedback (OFB). Control of the chaotic dynamics and possibility of suppressing the associated noise by sinusoidal modulation are investigated. The studies are based on numerical solutions of a time-delay rate equation model. The deterministic modulation dynamics of the laser are classified into seven regular and irregular dynamic types. Variations of chaotic dynamics and noise with sinusoidal modulation are examined in both time and frequency domains over wide ranges of the modulation depth and frequency. The results showed that chaotic dynamics can be converted into five distinct dynamic types; namely, continuous periodic signal (CPS), continuous periodic signal with relaxation oscillations (CPSRO), periodic pulse (PP), periodic pulse with relaxation oscillations (PPRO) and periodic pulse with period doubling (PPPD). The relative intensity noise (RIN) of these types is characterized when the modulation frequencies are much lower, comparable to, and higher than the resonance frequency. Suppression of RIN to a level 8 dB/Hz higher than the quantum limit was predicted under the CPS type when the modulation frequency is 0.9 times the resonance frequency and the modulation depth is 0.14.     

基于小波变换的光混沌信号消噪与Lyapunov指数计算

针对动力学方程未知且信噪比小的光混沌信号,采用小波多分辨分解算法对其进行噪音消减.用Lorenz混沌信号对该算法的消噪效果进行了检验.提出利用互信息量法和Cao氏法来改进小数据量法在时间延迟和嵌入维数计算上存在的主观选择性,对经过噪音消减的Lorenz混沌信号利用此改进的小数据量法计算其最大Lyapunov指数.结果表明,信噪比可提高近10 dB左右,最大Lyapunov指数计算误差可减少近30%,并求得半导体放大器光混沌信号的最大Lyapunov指数为0.389 6.     

基于最优滤波器的强混沌背景中谐波信号检测方法研究

强混沌背景中的微弱谐波信号检测有重要的工程研究意义. 目前的检测方法主要是基于Takens理论的混沌相空间重构方法, 然而这些方法往往对信干噪比要求高, 且对高斯白噪声敏感等. 本文注意到混沌信号的二阶统计特性是不变的, 根据这个特点提出了一种基于最优滤波器的强混沌背景中的微弱谐波信号检测方法. 该方法首先构建一个数据矩阵, 在频域上对每个频率通道分别检测谐波信号, 从而将信号检测问题转化为最优化问题, 然后利用最优化理论设计滤波器, 使待检测频率通道的信号增益保持不变, 而尽量抑制其他频率通道的信号, 最后通过判断每一频率通道的输出信干噪比来检测谐波信号. 与传统方法相比, 本文方法有如下优点: 1)可以检测更低信干噪比下的微弱谐波信号; 2)可检测的信号幅度范围更大; 3)抗白噪声性能更强. 仿真结果证明了本文方法的有效性.     

混沌干扰中基于同步挤压小波变换的谐波信号提取方法

针对混沌干扰背景下多个谐波信号的提取问题, 提出了一种基于同步挤压小波变换(SST)的谐波信号抽取方法. 首先利用SST将混沌信号和谐波信号组成的混合信号分解为不同的内蕴模态类函数, 然后利用Hilbert变换对分离出的内蕴模态类函数进行频率识别, 从中分离出各谐波信号. 以Duffing混沌背景为例, 对混沌干扰下多谐波信号的提取进行了实验分析. 实验结果表明: 对于不同频率间隔的多个谐波分量, 本文方法的提取结果都具有较高的精度, 而且所提方法对高斯白噪声的干扰具有较好的鲁棒性, 综合提取效果优于经典的经验模态分解方法.     

Blind Extraction of Chaotic Signals by Using the Fast Independent Component Analysis Algorithm

We report the results of using the fast independent component analysis （FastICA） algorithm to realize Mind extraction of chaotic signals. Two cases are taken into consideration： namely, the mixture is noiseless or contaminated by noise. Pre-whitening is employed to reduce the effect of noise before using the FastICA algorithm. The correlation coefficient criterion is adopted to evaluate the performance, and the success rate is defined as a new criterion to indicate the performance with respect to noise or different mixing matrices. Simulation results show that the FastICA algorithm can extract the chaotic signals effectively. The impact of noise, the length of a signal frame, the number of sources and the number of observed mixtures on the performance is investigated in detail It is also shown that regarding a noise as an independent source is not always correct.     

Identification of deterministic chaos by an information-theoretic measure of the sensitive dependence on the initial conditions

One of the most difficult problems in the field of non-linear time series analysis is the unequivocal characterization of a measured signal. We present a practicable procedure which allows to decide if a given time series is pure noise, chaotic but distorted by noise, purely chaotic, or a Markov process. Furthermore, the method gives an estimate of the Kolmogorov-Sinai (KS) entropy and the noise level. The procedure is based on a measure of the sensitive dependence on the initial conditions which is called ε-information flow. This measure generalizes the concept of KS entropy and characterizes the underlying dynamics. The ε-information flow is approximated by the calculation of various correlation integrals.     

混沌海杂波背景下的微弱信号检测混合算法

基于经验模态分解理论, 提出了一种基于粒子群算法的支持向量机预测方法. 采用总体平均经验模式分解法将混沌信号分解为若干固有模态函数和趋势分量, 将复杂的非线性信号转化为具有不同尺度特征的平稳分量. 利用粒子群算法对支持向量机的惩罚系数和核函数进行优化, 结合支持向量机建立混沌序列的单步预测模型. 从预测误差中检测淹没在混沌背景中的微弱信号(包括瞬态信号和周期信号). 对Lorenz系统和实测IPIX雷达数据进行仿真实验, 结果表明, 该方法能够有效地从混沌背景噪声中检测出微弱目标信号, Lorenz系统得到的均方根误差0.000000339 (-102.8225 dB时)比传统支持向量机方法的均方根误差0.049 (-54.60 dB时)降低了5个数量级, 从海杂波中检测出具有谐波特性的微弱信号, 表明预测模型具有更低的门限和误差.     

Comodulation masking release as a function of bandwidth and time delay between on-frequency and flanking-band maskers

The threshold for a signal masked by a narrow band of noise centered at the signal frequency (the on-frequency band) may be reduced by adding to the masker a second band of noise (the flanking band) whose envelope is correlated with that of the first band, an effect called comodulation masking release (CMR). This paper examines CMR as a function of masker bandwidth and time delay between the envelopes of the on-frequency and flanking bands. The 1.0-kHz sinusoidal signal had a duration of 400 ms. The on-frequency band was presented alone (reference condition) or with the flanking band. The flanking-band envelope was either correlated or uncorrelated with that of the on-frequency band. Flanking-band center frequencies ranged from 0.25-2.0 kHz. The flanking band was presented either in the same ear as the on-frequency band (monaural condition) or in the opposite ear (dichotic condition). The noise bands had bandwidths of 6.25, 25, or 100 Hz. In the correlated conditions, the flanking-band envelope was delayed with respect to that of the on-frequency band by 0, 5, 10, or 20 ms. For the 100-Hz bandwidth, CMRs were small (typically less than 1 dB) in both monaural and dichotic conditions at all delay times. For the 25-Hz bandwidth, CMRs were about 3.5 dB for the 0-ms delay, and decreased to about 1.5 dB for the 20-ms delay. For the 6.25-Hz bandwidth, CMRs averaged about 5 dB and were almost independent of delay time. The results suggest that the absolute delay time is not the critical variable determining CMR. The magnitude of CMR appears to depend on the correlation between the envelopes of the on-frequency and flanking bands. However, the results do not support a model of CMR that assumes that signal threshold corresponds to a constant change in across-band envelope correlation when the correlation is transformed to Fisher's z.     

A speech enhancement method based on Kalman filtering

I.IntroductionKa1manfilteringisjustamethodtoestimatestatistica1lythestateoftheobservedsystemfromthecorruptedsigna1s,andthiskindofcstimationisarecurrcneeestimationbasedon1inear,nonbiasandminimumvariance.Moreover,Ka1manfilteringisapplicabletonon-sta-honarysignalsandtime-variantdynamicsystem.Therefore,Kalmanfilteringisveryapplica-bletoenhancingthespeechsigna1sthatarecorruptedbynoise.ThispaperreportStheconcretcmethodofenhanccmentofnoisyspccchanditscxperimentresults.Experimentsindicate:Afterthes…     

利用小波多尺度分解算法实现混沌系统的噪声减缩

应用小波多尺度分解算法进行噪声减缩,从混沌背景中分离周期信号、噪声及其他混沌信号.小波多尺度分解算法能够区分不同尺度的信号是利用小波变换在时、频两域具有突出信号特征的能力以及小波变换是一线性变换的特点.提出的方法仅利用信号的尺度特性,克服了先前的噪声减缩要知道产生混沌信号的数学模型,并且要求叠加在混沌背景中的其他信号的幅度相对混沌背景信号的幅度很小的假定.给出了从Lorenz混沌背景中提取正弦信号、白噪声和Chua's电路产生的混沌信号的计算机模拟结果. 关键词：     

Detecting variation in chaotic attractors

If the output of an experiment is a chaotic signal, it may be useful to detect small changes in the signal, but there are a limited number of ways to compare signals from chaotic systems, and most known methods are not robust in the presence of noise. One may calculate dimension or Lyapunov exponents from the signal, or construct a synchronizing model, but all of these are only useful in low noise situations. I introduce a method for detecting small variations in a chaotic attractor based on directly calculating the difference between vector fields in phase space. The differences are found by comparing close strands in phase space, rather than close neighbors. The use of strands makes the method more robust to noise and more sensitive to small attractor differences.     

Comparison of nonlinear dynamic methods and perturbation methods for voice analysis

Nonlinear dynamic methods and perturbation methods are compared in terms of the effects of signal length, sampling rate, and noise. Results of theoretical and experimental studies quantitatively show that measurements representing frequency and amplitude perturbations are not applicable to chaotic signals because of difficulties in pitch tracking and sensitivity to initial state differences. Perturbation analyses are only reliable when applied to nearly periodic voice samples of sufficiently long signal lengths that were obtained at high sampling rates and low noise levels. In contrast, nonlinear dynamic methods, such as correlation dimension, allow the quantification of chaotic time series. Additionally, the correlation dimension method presents a more stable analysis of nearly periodic voice samples for shorter signal lengths, lower sampling rates, and higher noise levels. The correlation dimension method avoids some of the methodological issues associated with perturbation methods, and may potentially improve the ability for real time analysis as well as reduce costs in experimental designs for objectively assessing voice disorders.