随机噪声信号的功率谱估计的仿真

在通讯与电子信息工程行业及领域中,大部分问题的解决需要进行估计一个随机信号在频率域上的功率谱分布,诸如此类的问题有很多,比如：设计滤波器消除噪声信号,振动随机信号的回波抵消,随机信号的特征抽取与表示等等。功率谱估计的分类：一般分为两大类,一类是参数法功率谱估计,一类是非参数法功率谱估计。参数法功率谱估计通常对数据进行一种建模,比如把数据建模成滑动平均模型（Moving Average）,或者自回归（Autoregressive）模型,而非参数法功率谱估计。除了要求信号满足广义平稳之外,不需要其它的统计假设。与非参数法相比较,参数法的优点是在一个给定的数据集合上能够有较少的误差、偏差与方差。     

Minimax estimation of unknown deterministic signals in colorednoise

The estimation of a deterministic signal corrupted by random noise is considered. The strategy is to find a linear noncausal estimator which minimizes the maximum mean square error over an a priori set of signals. This signal set is specified in terms of frequency/energy constraints via the discrete Fourier transform. Exact filter expressions are given for the case of additive white noise. For the case of additive colored noise possessing a continuous power spectral density, a suboptimal filter is derived whose asymptotic performance is optimal. Asymptotic expressions for the minimax estimator error are developed for both cases. The minimax filter is applied to random data and is shown to solve asymptotically a certain worst-case Wiener filter problem     

Influence of smoothing window length on electromyogram amplitudeestimates

A systematic, experimental study of the influence of smoothing window length on the signal-to-noise ratio (SNR) of electromyogram (EMG) amplitude estimates is described. Surface EMG waveforms were sampled during nonfatiguing, constant-force, constant-angle contractions of the biceps or triceps muscles, over the range of 10%-75% maximum voluntary contraction. EMG amplitude estimates were computed with eight different EMG processor schemes using smoothing length durations spanning 2.45-500 ms. An SNR was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Over these window lengths, average ± standard deviation SNR's ranged from 1.4±0.28 to 16.2±5.4 for unwhitened single-channel EMG processing and from 3.2±0.7 to 37.3±14.2 for whitened, multiple-channel EMG processing (results pooled across contraction level). It was found that SNR increased with window length in a square root fashion. The shape of this relationship was consistent with classic theoretical predictions, however none of the processors achieved the absolute performance level predicted by the theory. These results are useful in selecting the length of the smoothing window in traditional surface EMG studies. In addition, this study should contribute to the development of EMG processors which dynamically tune the smoothing window length when the EMG amplitude is time varying     

Intrinsic FM noise of Gunn oscillators

It has been experimentally shown that the product of the mean frequency deviation of FM noise and the external quality factor varies inversely proportional to the power output of Gunn oscillators. This behavior of FM noise of Gunn oscillators with power output is in very good agreement with theoretical predictions of large-signal diffusion noise in GaAs devices. At optimum power output, the calculated and experimentally determined noise measure ranges between 17 and 22 dB.     

基于数字滤波的低频随机信号实时功率谱分析仪的实现

对于非平稳随机信号的功率谱分析,采用自相关函数傅里叶变换和直接傅里叶变换分析时,由于存在数据截断,二者从概念上都不能很好地反映随机信号的功率谱.采用参数谱估计方法,则不能实现等百分比带宽分析.对于噪声和结构振动等信号,需要进行等百分比带宽分析.采用滤波法进行功率谱分析,不存在数据截断带来的误差,还可方便地用于等百分比带宽的谱分析.利用数字滤波器可以实现时分和频分复用的特点,结合重抽样技术来实现整个频率轴上的实时功率谱分析.上述算法可以通过高速数字信号处理器(DSP)芯片实时地完成.文中介绍了基于数字滤波的低频随机信号实时功率谱分析仪的实现方法,并给出了实时算法的设计要点.     

Multistage adaptive filters for in-phase processing ofline-frequency signals

The authors describe two adaptive multistage digital filters for 50/60-Hz line-frequency signal processing in zero-crossing detectors and synchronous power systems. These filters combine a median filter with adaptive predictors, either finite-impulse response (FIR)- or infinite-impulse response (IIR)-based, thus making it possible to extract the sinusoidal signals from noise and strong disturbances without phase shifting the primary frequency signal. The median filter is used as a prefilter because it can remove deep commutation notches from the waveform. Adaptation allows the filters to track the exact instantaneous line frequency and avoids the selectivity problem encountered with a fixed filter     

Power spectral density of unevenly sampled data by least-squareanalysis: performance and application to heart rate signals

This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. The authors have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). It is concluded that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals the authors have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis     

Adaptive whitening of the electromyogram to improve amplitude estimation

Previous research showed that whitening the surface electromyogram (EMG) can improve EMG amplitude estimation (where EMG amplitude is defined as the time-varying standard deviation of the EMG). However, conventional whitening via a linear filter seems to fail at low EMG amplitude levels, perhaps due to additive background noise in the measured EMG. This paper describes an adaptive whitening technique that overcomes this problem by cascading a nonadaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies, subjects used real-time EMG amplitude estimates to track a uniform-density, band-limited random target. With a 0.25-Hz bandwidth target, either adaptive whitening or multiple-channel processing reduced the tracking error roughly half-way to the error achieved using the dynamometer signal as the feedback. At the 1.00-Hz bandwidth, all of the EMG processors had errors equivalent to that of the dynamometer signal, reflecting that errors in this task were dominated by subjects' inability to track targets at this bandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminish the advantages of whitening.     

Noise Attributes of LWIR HDVIP HgCdTe Detectors

Time series and Fourier-transform data on high-density vertically integrated photodiodes (HDVIP) at 0 and 50 mV reverse bias in the dark have been studied. The detectors have a cutoff wavelength λ _c (60 K) of 10.5 μm. Examination of the detector current time series and Fourier-transform curves of these devices reveals a variety of interesting characteristics: (i) time series displaying switching between four states characteristic of random telegraph signal (RTS) noise, the noise current power spectrum having Lorentzian or double Lorentzian type characteristics, (ii) time series data exhibiting wave-like characteristics with the noise current power spectrum being 1/f ²-like at low frequencies, (iii) time series having a mean value independent of time with the noise current power spectrum being white, and (iv) time series nearly independent of time with the noise current power spectrum having 1/f characteristics. Although from a single array, the excess noise characteristics at low (mHz) frequencies were varied, most of the detectors measured fell into one of these four categories. The predominance of detectors examined had minimal excess low-frequency noise down to ~ 10 mHz. Detectors that displayed RTS noise in reverse bias were repeatable under subsequent measurement. However, when measured at zero bias, the same detectors exhibited no RTS noise, the noise current power spectrum being white in nature.     

Gait phase information provided by sensory nerve activity duringwalking: applicability as state controller feedback for FES

In this study, we extracted gait-phase information from natural sensory nerve signals of primarily cutaneous origin recorded in the forelimbs of cats during walking on a motorized treadmill. Nerve signals were recorded in seven cats using nerve cuff or patch electrodes chronically implanted on the median, ulnar, and/or radial nerves. Features in the electroneurograms that were related to paw contact and lift-off were extracted by threshold detection. For four cats, a state controller model used information from two nerves (either median and radial, or ulnar and radial) to predict the timing of palmaris longus activity during walking. When fixed thresholds were used across a variety of walking conditions, the model predicted the timing of EMG activity with a high degree of accuracy (average error = 7.8%, standard deviation = 3.0%, n = 14). When thresholds were optimized for each condition, predictions were further improved (average error = 5.5%, standard deviation = 2.3%, n = 14). The overall accuracy with which EMG timing information could be predicted using signals from two cutaneous nerves for two constant walking speeds and three treadmill inclinations for four cats suggests that natural sensory signals may be implemented as a reliable source of feedback for closed-loop control of functional electrical stimulation (FES).     

Impact of ambient temperature set point deviation on Arrhenius estimates

A least squares model is developed that takes ambient temperature set point error into account when making predictions with the Arrhenius equation. Complete data following a lognormal distribution is assumed. According to the model, the regression can be performed assuming the temperatures are at their desired levels (fixed temperature case). Random variation of the actual ambient temperature about the set point value will inflate the variance or mean squared error but will not bias the estimates. This increases the width of confidence intervals on the parameter estimates and predictions like the MTTF compared to the fixed temperature case. The amount of inflated variance depends chiefly on the extent of set point deviation and activation energy but is also influenced by choice of experimental design.     

Controlled noise generation with avalanche diodes I. Low pulse rate design

The generation of random noise with uniform avalanche diodes is investigated. A special avalanche noise diode is designed in which the pulse rate is determined by internal field emission and circuit constants, resulting in a small and predictable temperature dependence of the noise output. The spectral noise distribution is calculated for the case of a clipped sawtooth wave with small random fluctuations of the time between successive pulses. The resulting spectral distribution can, to a good approximation, be expressed as a line spectrum with overlapping harmonics. Under certain experimental conditions the harmonics overlap sufficiently that white noise is obtained over a wide frequency range, e.g. from 40 kc/s to 200 kc/s or more. The experimental results are in very good agreement with the theoretical predictions.     

SSDA波形匹配在高频电源开关控制中的应用

针对高频电源开关控制问题,提出改进序贯相似度检测(SSDA)的波形匹配方法来预测一次电流过零点,以控制高频电源开关元件开通和关断来提高高频电源输出功率。通过对SSDA算法阈值设定方法的改进,提高了该算法的匹配速度。首先,对不同条件下的一次电流波形进行取点,考虑波形采集过程的噪声,依次采取低通滤波和算术平均滤波的方法对采样数据进行处理后建立波形库。利用改进SSDA波形匹配的方法,根据匹配相似度较高的库中波形来预测目标波形的过零点时间,从而控制电源开关。实验结果证明,所提方法具有可实施性,且有较快的匹配速度以及较高的匹配精度。     

激光导引头四象限探测器测角精度的建模研究

四象限探测器对目标的测角精度直接影响激光制导武器的制导精度,因此研究四象限探测器测角精度十分重要。采用计算和仿真分析的方法,基于导引头入瞳处脉冲峰值功率密度服从均匀分布,噪声干扰电流服从高斯分布,建立了激光导引头光束偏转角误差模型;但由于噪声干扰使光束偏转角误差具有随机概率性,所以将光束偏转角误差的均值和标准差作为四象限探测器测角精度的衡量标准,分别建立了光束偏转角误差的均值和标准差与导引头光学系统参数、光斑半径、漫反射激光入射角、噪声干扰电流以及导引头入瞳处脉冲峰值功率密度之间的关系,并以导引头光束偏转角误差标准差为例,结合应用背景进行了仿真分析。     

Fatigue compensation of the electromyographic signal for prostheticcontrol and force estimation

During a sustained muscle contraction, the amplitude of electromyographic (EMG) signals increases and the spectrum of the EMG signal shifts toward lower frequencies. These effects are due to muscular fatigue and can cause problems in the control of myoelectric prostheses and in the estimation of contraction level from the EMG signal. It has been well known that the fatigue effects can be explained by the conduction velocity changes during the fatigue process and by the idea that the conduction velocity is linearly proportional to the median frequency of EMG signals. Hence the fatigue process can be monitored by measuring the median frequency. A fatigue compensation preprocessor has been developed. It uses the widely accepted power spectrum density model of EMG signals that contains the conduction velocity as a measure of fatigue. It was verified that the preprocessor scales down the amplitude of the fatigued EMG signal and decompresses the spectrum. Hence, the preprocessor eliminates the increase in amplitude and the shift in frequency and enables consistent EMG signals to be used to control prostheses     

Frequency noise measurement of diode-pumped Nd:YAG ring lasers

The combined frequency noise spectrum of two lightwave Model 120-01A nonplanar ring oscillator lasers was measured by first heterodyne detecting the IF signal and then measuring the IF frequency noise using an RF frequency discriminator. The results indicated the presence of a 1/f² noise component in the power spectral density of the frequency fluctuations between 1 Hz and 1 kHz. After incorporating this 1/f² into the analysis of the optical phase tracking loop, the measured phase error variance closely matches the theoretical predictions     

Frequency measurement using the phase-controlled oscillator

This paper describes the application of the phase-controlled oscillator to precision frequency comparison in order to improve the measurement accuracy. A relation derived between the counter readings, which measure the average frequency over time T, and the power spectrum of the added phase noise forms the basis for the design of the loop in various applications: frequency comparison, optimization of the operating parameters of frequency standards by detecting the frequency shift due to an applied parameter change, and assessment of the phase jitter generated in multiplier chains. The design procedure includes a determination of the error due to drifts in the oscillator and an estimate of the response time of the loop. An example of a loop which multiplies a noisy 5.6 kHz signal by 200 is given. In determining the mean frequency of a 9.2 MHz crystal oscillator compared to a rubidium vapor frequency standard the one-second counter readings of the multiplied beat signal are found to have a standard deviation of 1.16 × 10^-11.     

Circuit for Monitoring the Median Frequency of the Spectrum of the Surface EMG Signal

The power spectrum of the surface EMG signal is known to undergo a compression towards the lower frequencies during sustained muscle contractions. The median frequency appears to be the preferred parameter to monitor this compression. This paper describes a simple circuit which can provide an estimate of the median frequency.     

宽带数据采集系统的相位噪声分析

研究了宽带数据采集系统的相位噪声、信号频率和信噪比之间的关系。根据相位噪声的计算模型,推导出多载频信号和宽带信号相位抖动信噪比的计算方法,通过噪声的频谱可以看出,独立时钟相位的抖动噪声是均匀分布在整个采样频谱中的,过采样和滤波可有效改善其信噪比。理论结果表明线性调频信号的相位噪声可用宽带信号的模型计算,当信号的带宽较小时,完全可由单频信号的计算公式得出近似的结果。最后,仿真结果验证了该SNR算法的有效性。