Comparison of time-frequency distribution techniques for analysisof simulated Doppler ultrasound signals of the femoral artery

The time-frequency distribution of the Doppler ultrasound blood flow signal is normally computed by using the short-time Fourier transform or autoregressive modeling. These two techniques require stationarity of the signal during a finite interval. This requirement imposes some limitations on the distribution estimate. In the present study, three new techniques for nonstationary signal analysis (the Choi-Williams distribution, a reduced interference distribution, and the Bessel distribution) were tested to determine their advantages and limitations for analysis of the Doppler blood flow signal of the femoral artery. For the purpose of comparison, a model simulating the quadrature Doppler signal was developed, and the parameters of each technique were optimized based on the theoretical distribution. Distributions computed using these new techniques were assessed and compared with those computed using the short-time Fourier transform and autoregressive modeling. Three indexes, the correlation coefficient, the integrated squared error, and the normalized root-mean-squared error of the mean frequency waveform, were used to evaluate the performance of each technique. The results showed that the Bessel distribution performed the best, but the Choi-Williams distribution and autoregressive modeling are also techniques which can generate good time-frequency distributions of Doppler signals     

A signal-dependent time-frequency representation: optimal kerneldesign

A new time-frequency distribution (TFD) that adapts to each signal and so offers a good performance for a large class of signals is introduced. The design of the signal-dependent TFD is formulated in Cohen's class as an optimization problem and results in a special linear program. Given a signal to be analyzed, the solution to the linear program yields the optimal kernel and, hence, the optimal time-frequency mapping for that signal. A fast algorithm has been developed for solving the linear program, allowing the computation of the signal-dependent TFD with a time complexity on the same order as a fixed-kernel distribution. Besides this computational efficiency, an attractive feature of the optimization-based approach is the ease with which the formulation can be customized to incorporate application-specific knowledge into the design process     

The time-frequency distributions of nonstationary signals based ona Bessel kernel

A kernel based on the first kind Bessel function of order one is proposed to compute the time-frequency distributions of nonstationary signals. This kernel can suppress the cross terms of the distribution effectively. It is shown that the Bessel distribution (the time-frequency distribution using Bessel kernel) meets most of the desirable properties with high time-frequency resolution. A numerical alias-free implementation of the distribution is presented. Examples of applications in time-frequency analysis of the heart's sound and Doppler blood flow signals are given to show that the Bessel distribution can be easily adapted to two very different signals for cardiovascular signal processing. By controlling a kernel parameter, this distribution can be used to compute the time-frequency representations of transient deterministic and random signals. The study confirms the potentials of the proposed distribution in nonstationary signal analysis     

Adaptive diffusion as a versatile tool for time-frequency and time-scale representations processing: a review

Inspired by the work on image processing by Perona and Malik, diffusion-based models were first investigated by Goncalve/spl grave/s and Payot to improve the readability of Cohen class time-frequency representations. They rely on signal-dependent partial differential equations that yield adaptive smoothed representations with sharpened time-frequency components. Here, we demonstrate the versatility and utility of this family of methods, and we propose new adaptive diffusion processes to locally control both the orientation and the strength of smoothing. Our approach is an improvement on previous works as it provides a unified framework not only for the Cohen class but for the affine class as well. The latter is of particular interest because, except for some special techniques such as the reassignment method, no signal-dependent smoothing technique exists to process bilinear time-scale distributions, nor even a transposition of the adaptive optimal-kernel method proposed by Baraniuk and Jones.     

Nonstationarity broadening reduction in pulsed Doppler spectrummeasurements using time-frequency estimators

The spectral width of Doppler signals is used as measure of lesion-induced flow disturbance. Its estimation accuracy is compromised using the conventional short-term Fourier transform (STFT) since this method implicitly assumes signal stationarity during the signal window while the Doppler signals from arteries are markedly nonstationary. The Wigner-Ville (WVD), Choi-Williams (CWD) and Bessel distributions (BD), specifically designed for nonstationary signals, have been optimized for spectral width estimation accuracy and compared to the STFT under different signal to noise ratios using simulated Doppler signals of known time-frequency characteristics. The optimum parameter values for each method were determined as a Hanning window duration of 10 ms for the STFT, 40 ms for the WVD and CWD and 20 ms for the BD and dimensionless time-frequency smoothing constant values of five in the CWD and two in the BD. Thresholding was used to reduce the effect of cross terms and side lobes in the WVD and BD. With no added noise the WVD gave the lowest estimation error followed by the CWD. At signal-to-noise ratios (SNRs) of 10 dB and 20 dB the CWD and BD had similar errors and were markedly better than the other estimators. Overall the CWD gave the best performance     

The Weyl correspondence and time-frequency analysis

Describes the Weyl correspondence and its properties, showing how it gives a “window-independent” definition of time-frequency concentration for use in models in signal detection. The definition of concentration is justified by showing that it gives reasonable answers in certain intuitive cases. The Weyl correspondence expresses a linear transformation as a weighted superposition of time-frequency shifts of the signal, and then authors explain why this is not the same as “transforming” a signal into the time-frequency domain, multiplying by a weight in the transform domain and taking the inverse. The investigation into time-frequency concentration and the Weyl correspondence is justified by a new result. The authors show that convolving the Wigner distribution with a general smoothing function is equivalent to evaluating a weighted sum of spectrograms. This is a new interpretation of the process of smoothing the Wigner distribution to reduce cross-terms. It relates smoothing of the Wigner distribution to the “multiple window” technique pioneered by Thomson (1982)     

Matching pursuits with time-frequency dictionaries

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive time-frequency transform. They derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. They compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected with the algorithm of Coifman and Wickerhauser see (IEEE Trans. Informat. Theory, vol. 38, Mar. 1992)     

空间高阶时频分布及虚拟时频ESPRIT算法

基于L-Wignrer分布(LWD),定义了一种全新的空间高阶累积量L-Wigner分布,并利用虚拟时频ESPRIT算法与阵列扩展,提出了一种多个多项式相位信号(PPS)的DOA估计算法.该方法利用平滑的伪WVD分布以实现PPS信号的LWD分布极大地抑制了其中的交叉项并提高了信号时频聚集性.同时,将时频分析与高阶累积量结合,使算法具有虚拟阵列扩展的特点,高分辨高精度的估计了PPS信号的DOA.理论分析和计算机仿真表明该方法具有很好的参数估计性能.     

Joint time-frequency analysis for radar signal and image processing

The Fourier transform has been widely used in radar signal and image processing. When the radar signals exhibit time- or frequency-varying behavior, an analysis that can represent the intensity or energy distribution of signals in the joint time-frequency (JTF) domain is most desirable. In this article, we showed that JTF analysis is a useful tool for improving radar signal and image processing for time- and frequency-varying cases. We applied JTF analysis to radar backscattering and feature extraction; we also examined its application to radar imaging of moving targets. Most methods of JTF analysis are non-parametric. However, parametric or model-based methods of time-frequency analysis, such as adaptive Gaussian and chirplets, are more suitable for radar signals and images     

The Analysis of Under Water Vehicle Non-Stationary Acoustic Transient Signals Using a New Fast Algorithm for Constructing Positive Time-Frequency Distributions

It is well know that under water vehicles emit many types of acoustic signals, including stationary as well as non-stationary. The non-stationary transient signals are receiving considerable attention, especially from the standpoint of detection and tracking of under water vehicles. Thus, modern methods for processing these non-stationary acoustic transient signals are required. This paper presents a very powerful method for analyzing acoustic transient signals, the positive time-frequency distribution. A fast algorithm that implements the minimum cross-entropy positive time-frequency distribution makes practical the processing of 'real world data', like under water vehicle acoustic transient signals. An example of such a signal is presented, which is a generic acoustic transient signal from a under water vehicle. The signal is only representative, as it is normalized in time, in frequency, and in amplitude. The positive time-frequency distribution constructed for this generic transient signal is contrasted with the one-third octave method, which is currently the primary method being used by the under water vehicle community to analyze under water vehicle transients. The positive distribution is also contrasted with broad band and narrow band spectrograms.     

Measuring time-frequency information content using the Renyientropies

The generalized entropies of Renyi inspire new measures for estimating signal information and complexity in the time-frequency plane. When applied to a time-frequency representation (TFR) from Cohen's class or the affine class, the Renyi entropies conform closely to the notion of complexity that we use when visually inspecting time-frequency images. These measures possess several additional interesting and useful properties, such as accounting and cross-component and transformation invariances, that make them natural for time-frequency analysis. This paper comprises a detailed study of the properties and several potential applications of the Renyi entropies, with emphasis on the mathematical foundations for quadratic TFRs. In particular, for the Wigner distribution, we establish that there exist signals for which the measures are not well defined     

Adaptive optimal kernel smooth-windowed Wigner-Ville bispectrum for digital communication signals

Higher-order time-frequency distribution (HO-TFD) outperforms the bilinear TFD in noisy conditions but suffers more severely from cross-terms when used to analyze multi-component signals. Various kernel functions have been introduced to suppress cross-terms in bilinear TFD but in general TFD with a fixed kernel do not give accurate TFR for all type of signals. In this paper, adaptive optimal TFR is obtained by extending the separable kernel design in bilinear TFD to the third-order TFD and is able to achieve accurate time-frequency representation at SNR as low as −2 dB. This globally adaptive optimal kernel smooth-windowed Wigner-Ville bispectrum (AOK-SWWVB) is designed where its separable kernel is determined automatically from the input signal, without prior knowledge of the signal parameters. It is shown that this system performance is comparable to the system when priori knowledge of the signal is known.     

Dopplerlet变换及其匹配收敛性分析

介绍了一种以伸缩、时移和频移后的加窗Doppler信号为基函数的时频信号表示Dopplerlet变换,证明了基于Dopplerlet基函数的自适应匹配投影塔形分解算法是收敛的,而且分解过程中所得残差信号的能量按指数律衰减。实验结果表明了以Dopplerlet为基函数的自适应匹配投影塔形分解法能更紧凑地表示自然界中的一类Doppler信号。     

基于Hough变换的任意时频分布线条特征提取

时频分布将时(空)域信号变换到时频联合域,揭示出信号的非平稳性、瞬时频率以及频率随时(空)间的变化演进,这其中往往主要含有线条成分,现有的分析方法只能探测直线成分(即线性调频参数),并且只是分析Wigner分布.本文进行两方面的推广,一方面将Hough变换探测各种参数曲线,可以对任意时频模式的信号进行检测;另一方面推广到任意时频分布,试验结果表明本文方法对于低信噪比的信号,检测可靠性更高,尤其对于自适应时频分布,效果更为满意.     

基于对称阵列Wigner-Ville分布的宽带线性调频信号AOA估计

本文提出了基于对称阵元Wigner-Ville分布(WVD)的宽带线性调频信号到达角(AOA)估计算法。该算法利用对称阵元输出延时参数的互补性和Wigner分布定义提取宽带信号方向向量,建立了新的空间时频矩阵。借助线性调频信号Wigner分布的良好时频聚集特性,适当选取时频点实现了对各个信号AOA的逐一估计。在新的空间时频矩阵模型基础上给出了基于信号子空间投影的AOA估计方法。它不需要对AOA的初始估计、聚汇和插值,减少了计算量,提高了精度,仿真实验证明了算法的有效性。     

On the Use of Spatial Time Frequency Distributions for Signal Extraction

This paper deals with the extraction of signals from their instantaneous linear mixtures using time-frequency distributions. Fundamentally, this problem is a signal synthesis from the time-frequency (t-f) plane. However with the incorporation of the spatial information provided by a multisensor array, the problem can be posed as special case of blind source separation. So far, the blind source separation has been solved using only statistical information available on the source signals. Herein, we propose to solve the aforementioned problem using time-frequency signal representations and the spatial array aperture. The proposed approach relies on the difference in the t-f signatures of the sources to be separated. It is based on the diagonalization of a combined set of spatial time-frequency distribution matrices. A numerical example is provided to illustrate the effectiveness of our method.     

无限方差噪声环境下的分数低阶空间时频盲源分离

对脉冲噪声α稳定分布环境下的时频分布进行了研究,改进了适合α稳定分布信号或强脉冲噪声环境的分数低阶时频分布方法,用分数低阶空间时频矩阵代替空间时频矩阵,基于时频盲分离算法提出了一种改进的分数低阶空间时频盲源分离算法,并归纳了算法步骤。通过对FLO-TF-UBSS算法和已有的TF-UBSS算法及MD-BSS算法进行详细比较,仿真结果表明,所提出的FLO-TF-UBSS算法有效的降低了信号的均方误差（MSE）,能较好的对α稳定分布噪声环境下的非平稳信号进行盲分离,并实现了对实际的稳定分布舰船信号的盲提取,性能优于已有TF-UBSS算法和MD-BSS算法,且具有一定的韧性。      

Time-frequency window leakage in the short-time Fourier transform

Conventional frequency-domain window-leakage analysis accurately describes the leakage in the short-time Fourier transform only for stationary signals. Leakage in the time-frequency plane from concentrated transient or nonstationary signals can be effectively analyzed by use of a time-frequency window-leakage envelope with rectangular contours. This envelope is obtained from the Wigner distribution of the analysis window, with appropriate corrections for the sidelobe leakage. The time-frequency window-leakage envelope gives insight into the tradeoffs in time-frequency leakage between various windows and allows quick and accurate estimates of the leakage in the short-time Fourier transform.A simple technique for constructing signals with Wigner distributions that are linear transformations of the Wigner distribution of a known signal is developed. With this technique, windows with a variety of time-frequency orientations and leakage behavior can be developed.This work has been supported by NSF Grant No. ECS 83-14006 and by an NSF graduate fellowship.     

Shear madness: new orthonormal bases and frames using chirpfunctions

The proportional-bandwidth and constant-bandwidth time-frequency signal decompositions of the wavelet, Gabor, and Wilson orthonormal bases have attracted substantial interest for representing nonstationary signals. However, these representations are limited in that they are based on rectangular tessellations of the time-frequency plane. While much effort has gone into methods for designing nice wavelet and window functions for these frameworks, little consideration has been given to methods for constructing orthonormal bases employing nonrectangular time-frequency tilings. The authors take a first step in this direction by deriving two new families of orthonormal bases and frames employing elements that shear, or chirp, in the time-frequency plane, in addition to translate and scale. The new scale-shear fan bases and shift-shear chevron bases are obtained by operating on an existing: wavelet, Gabor (1946), or Wilson basis set with two special unitary warping transformations. In addition to the theoretical benefit of broadening the class of valid time-frequency plane tilings, these new bases could possibly also be useful for representing certain types of signals, such as chirping and dispersed signals     

基于重叠变换和时频分析的自适应抗干扰算法

将时频分析和重叠变换结合,提出了一种新的直接序列扩频通信窄带干扰抑制算法,通过重叠变换将多分量信号分解成为一系列单分量信号并通过维格纳分布法确定单分量干扰的频率。在此基础上设计了一自适应滤波器抑制掉干扰。这种方法对平稳信号和非平衡信号同样适用。给出相应的计算机仿真结果,并与其它变换域算法进行了比较。