循环谱密度的两通道最大熵谱估计

对周期平稳随机过程的循环谱密度的估计,过去主要采用时域平均周期图谱法和频域平滑周期图法,但当短数据时,上述方法的分辨率很低,方差很大,本文提出了循环谱密度的两通道最大熵谱估计,该方法不仅具有很高的分辨率,而且估计的方差也很小,具有较好地估计性能。     

认知无线电中的宽频段频谱感知器设计

无线频谱感知器是认知无线电系统的核心。文章针对认知无线电中的宽频段频谱感知问题,提出了利用谐波滤波器组周期性检测的方案,并给出了基于能量检测的硬件实现框图。理论与仿真分析表明,基于快速傅里叶变换的周期图法不满足一致性估计条件,方差性能很难满足实际应用需求;分段平均周期图法与加窗平均周期图法收敛性好,方差小,但是功率谱密度主瓣较宽,分辨率低;多窗谱估计法具有较小的估计偏差,且其方差性能和频谱分辨率均远远优于周期图法,因而比较适合应用在实际的认知无线电系统中。     

测量噪声背景下微弱正弦信号参数估计的互功率谱方法

本文首次把近代谱估计方法引入到互谱估计中，从理论上证明了互相关函数的Ｙｕｌｅ－Ｗａｌｋｅｒ方程，并在此基础上提出了互谱参数谱估计的矩估计方法和Ｌｅｖｉｎｓｏｎ递推估计方法。该方法可以有效地克服传统的互谱ＦＦＴ算法和互周期图法存在的谱分辩率低，谱估计方差大等缺点。文中还给出了信噪比为－３０ｄＢ的正弦信号参数估计的仿真实例。     

多正弦窗谱估计的性能分析

多正弦窗谱估计是一种非参数的现代谱估计方法。本文较为全面地分析了它的性能,包括谱的聚集性,偏差,方差,最优窗口数,分辨率,计算复杂度等。以四种信号模型为例,分析了窗口数和小波阈值法去除估计噪声对多正弦窗谱估计的偏差和方差的影响,并与周期谱图进行了比较。理论分析和实验结果表明与周期谱图相比,多正弦窗谱具有小得多的偏差和方差,可将其应用于谱估计的性能要求较高的场合。     

基于多正弦窗谱估计的改进谱减法语音增强

采用运算简单、实时性好、改进的功率谱减法对带噪语音进行增强。鉴于谱减法的特点,文中采用了多正弦窗谱估计对功率谱进行估计。多正弦窗谱估计是一种非参数的现代谱估计方法,与周期图法相比,谱估计偏差和方差更小,可以得到效果更好的增强语音。分别对周期图法、多窗谱法及多正弦窗谱法的增强效果进行了比较。实验结果表明,语音在多正弦窗谱法增强后,分段信噪比最高,且没有因为谱减产生的音乐噪声,主观听觉效果最好。多正弦窗谱估计对输入信噪比较低的含噪语音增强效果非常好。     

低采样率高分辨率压缩功率谱估计方法的仿真研究

低采样率的宽带功率谱估计在很多领域具有应用价值.采用压缩多核采样结构得到信号的压缩测量值, 然后建立测量值相关函数与信号相关函数之间的关系, 用最小二乘法实现相关函数估计, 最后实现功率谱的估计.该压缩采样方法的等效采样率为M/N·f_s, 可在没有任何对时域或频域稀疏性的假设条件下降低采样率.仿真分析表明, 该方法的系统噪声与加性噪声性能比周期图法略有降低, 但只要系统设计合理, 对于一定信噪比的信号, 系统噪声与加性噪声基本可以忽略, 并给出了对应的理论分析.估计分辨率与周期图法相比, 等效长度相同时略有提高; 由于本文方法降低了测量值的数目, 对于一定长度的数据来说, 估计分辨率得到了极大的提高.本文方法适用于低信噪比信号的低采样率高分辨率功率谱估计.     

基于谱密度的分形高斯噪声数据合成和估计

分形高斯噪声FGN是分形布朗运动的增量过程，广泛应用于自相似过程的建模分析。在众多的合成分形高斯噪声的方法中，研究了基于功率谱的FGN的生成算法，谱模拟方法和Paxson方法的特点以及联系．同时对基于功率谱的Hurst指数的估计方法，周期图法进行了讨论。仿真试验中，使用Paxson方法生成了FGN序列数据．试验结果表明生成的FGN具有很好的自相似性。应用周期图法估计了其Hurst指数，相比方差时间法，周期图法在估计长相关过程的参数方面能够提高精度。     

基于多窗谱估计的改进谱减法

在各种语音增强法中,谱减法以其简单、有效而被广泛应用。在谱减法中,一般使用周期图法对语音信号进行功率谱估计,但是它的估计偏差以及方差较大,直接结果是引起了较大的音乐噪声。而多窗谱(multitaper)估计具有较小的估计偏差和方差。因此,使用基于多窗谱估计的改进谱减法可以有效降低音乐噪声。实验结果证明,在相同实验条件下,该方法得到的增强语音中的音乐噪声要明显小于使用周期图法的结果,且信噪比可提高1~2 dB。     

随机信号谱估计方法的Matlab实现

功率谱估计是随机信号分析中的一个重要内容。从介绍功率谱的估计原理入手分析经典谱估计和现代谱估计两类估计方法的原理、各自特点及在Matlab中的实现方法。经典功率谱估计的方差大、谱分辨率差,分辨率反比于有效信号的长度,但现代谱估计的分辨率不受此限制。给出了功率谱估计的应用。     

改进的循环谱估计快速算法与性能分析

该文提出了一种改进的循环谱估计快速算法,在估计性能不降低的条件下,减小了原有数字化频域平滑谱估计算法对数据量的要求。算法将时域平滑算法中数据加窗、重叠等处理方法引入频域算法,有效降低了估计方差,改善循环谱的估计效果。论文对改进算法渐进性均值、方差、谱分辨率的表达式进行推导。理论与实验结果分析表明:在相同数据量和谱估计分辨率的条件下,改进方法估计性能优于原算法,可用于实际低信噪比、小数据量、高分辨率要求下循环谱估计。     

The smoothed pseudo Wigner distribution in speech processing

The Wigner distribution is shown to be a useful tool for the analysis of speech data. Cross-terms which characterize the Wigner distribution of multi-component signals have been suppressed using the smoothed pseudo Wigner estimator. This estimator possesses several advantages over the short time periodogram. Comparison of the SPWD to that of the spectrogram reveals that much better resolution is achieved by the SPWD.     

Median-based cyclic polyspectrum estimation

In this paper, median-based estimation methods for the cyclic polyspectrum are proposed. The algorithms do not require a priori knowledge of the β submanifolds, that is, they do not require the knowledge of all of the lower order cycle frequencies of the time-series available for the estimation of the cyclic polyspectrum. Therefore, such methods are particularly useful when the cyclostationarity of the signals under consideration is not completely known. The proposed estimators converge to the theoretical values of the cyclic polyspectrum when the collect time approaches infinity and the spectral resolution becomes infinitesimal. Furthermore, their accuracy is very nearly the same as that of the usual time- and frequency-smoothed cyclic periodogram methods that use a priori knowledge of lower-order cycle frequencies to avoid the β submanifolds     

Evaluation of FFT-Based and Moder Parametrc Methods for the Spectral Analysis of Bioprosthetic Valve Sounds

The objective of this paper is to compare the performance of conventional FFT-based (basic periodogram and Welch's method) and modern parametric (all-pole and pole-zero modeling) methods in estimating the spectral distribution of cardiac bioprosthetic valve sounds, and for the extraction of the two most dominant frequency peaks (DFP). These methods were tested for stability by adding random noise and truncating the bioprosthetic valve closing sounds, and for reproducibility by measuring the variance of the spectra obtained from three consecutive recordings of each patient. Results from a group of 11 patients show that the basic periodogram and Steiglitz-McBride's method with maximum entropy (pole-zero modeling) provide the most consistent (minimal variance) estimates of the DFP's of the closing sounds. However, for estimating spectral distributions, the most stable methods appear to be the basic periodogram and Steiglitz-McBride's method with extrapolation to zero. The basic periodogram appears to be the best compromise to estimate both the spectral distribution and the DFP's of the bioprosthetic closing sounds.     

Multitaper estimation of the innovation variance of a stationarytime series

Accurate computation of the innovation variance of a stationary time series by a nonparametric method provides useful information to judge the quality of fit of parametric models for the time series. Previous estimators of the innovation variance have made use of raw periodogram ordinates, smoothed periodogram ordinates, or periodogram ordinates following tapering. Smoothing provides more degrees of freedom at each frequency but fewer independent estimates, whereas tapering reduces side-lobe leakage if the dynamic range of the spectrum is high but produces only two degrees of freedom at each frequency. Here, we investigate estimation of innovation variance from finite sample sizes by the use of multiple tapering. The tapers are designed to reduce side-lobe leakage and produce increased degrees of freedom at each frequency. It is demonstrated that the multiple tapering approach produces much better estimates of the innovations variance than the other methods when the spectrum has a high dynamic range and/or is rapidly varying. The multitaper bandwidth parameter W may be selected using an obvious heuristic approach or by an automatic method. The multitaper method is hence an attractive alternative to conventional techniques     

Choix entre les différentes méthodes quadratiques d’estimation du spectre de puissance

Quadratic estimators of power spectra are compared, for a given total observation range, by using the matrix of the associated quadratic form. Considering the case of smooth spectra, it is shown that the smallest variance for a given resolution is achieved if this matrix is Toeplitz. From the estimation and the computation points of view, a detailed comparison is made between two types of estimation schemes. The first is composed of methods equivalent to the smoothing of a single periodogram; the second reduces to the very popular Welch method (averaging of several short, modified periodograms). Our presentation makes clear the choice of parameters and should prevent errors, common mainly when dealing with short records.     

Estimation of the Walsh spectrum (Corresp.)

In this work We investigate estimates of the Walsh spectrum of a dyadic-stationary series. We discuss the Walsh periodogram and smoothed periodogram computed from a stretch{X(t), t in [0, T]}of such a series.     

LFMCW雷达运动目标距离与速度超分辨估计

超分辨谱估计算法能得到比传统周期图法高得多的分辨率,针对LFMCW雷达动目标检测问题,本文提出一种基于状态矢量空间方法的LFMCW雷达距离与速度超分辨估计方法。文中介绍了状态矢量空间方法的基本原理并分析了三角LFMCW雷达上、下扫频段差频信号的特点,使用基于状态矢量空间方法估计差频信号频谱,同时给出了相应的运动目标距离与速度的估计算法。该算法解决了LFMCW雷达动目标去耦问题,与传统FFT方法相比提高了运动目标距离与速度的分辨率和估计精度。仿真结果证明了该算法的有效性。      

Approximate variances for tapered spectral estimates

We propose an approximation of the asymptotic variance that removes a certain discontinuity in the usual formula for the raw and the smoothed periodogram in case a data taper is used. It is based on an approximation of the covariance of the (tapered) periodogram at two arbitrary frequencies. Exact computations of the variances for a Gaussian white noise and an AR(4) process show that the approximation is more accurate than the usual formula.     

时间重叠的循环谱估计性能分析

通过分析加ssinc窗的循环周期图在谱频率方向和循环频率方向的偏差，分析了TA循环谱估计是无偏估计。然后，根据STFT方法，推导了使方差得以减小的平均循环周期图的计算方法，同时分析了加ssinc窗的平均循环谱估计能使方差得到最佳地减少，并采用平均的TA循环谱估计方法，估计了具有不同循环频率的两个循环平稳信号和的循环谱，所得的循环谱能在循环频率方向将各循环平稳信号准确地分离开，并在各循环频率处的谱频率方向准确得到各自的功率谱。     

时间重叠的循环谱估计

首次将时间重叠法和ssinc窗应用于循环谱估计中,提出了时间重叠的循环谱估计方法,使循环谱估计取得了突破性的进展(:1)循环谱的偏离误差减小,有效频率分辨率提高,能准确地分辨出循环频率方向和谱频率轴方向的谱峰;(2)计算量大大减少。通过时间的重叠过程,减少DFT要求的数据量,提高了循环谱估计的实时性。