基于稀疏时频分布的跳频信号参数估计

基于常规时频分析方法的跳频信号参数估计中,采用核函数抑制时频分布交叉项会导致时频聚集性的下降,不利于信号参数提取。针对此问题,该文提出一种基于稀疏时频分布(STFD)的跳频信号处理方法。该方法首先根据Cohen类分布的原理和跳频信号模糊函数的特点,以模糊域矩形窗为核函数,构建了一种Cohen类的矩形核分布(RKD)。RKD可有效抑制交叉项,但其时频分辨率较低。为提高RKD的时频性能,在压缩感知框架下,利用跳频信号时频分布的稀疏特性,对RKD附加稀疏性约束,建立稀疏时频分布(STFD)的优化求解模型。STFD不仅能有效抑制交叉项,而且具有良好的时频聚集性。仿真分析表明,与传统时频分析方法相比,该文提出的基于STFD的跳频信号参数估计方法性能更优。     

一种新的二次时频分布和几种主要二次时频分布的比较研究

本文提出一种新的Ｃｏｈｅｎ类时频分布并对几种主要Ｃｏｈｅｎｏ类频分布进行了实验比较研究。结果表明，基于指数分布和锥形核分布和复合核分布具有更强的抑制交叉干扰性质，同时几乎不使ＥＤ或ＣＤＫ的时频分辨力降低。     

二次时频表示中核函数的优化设计

二次时频分布是分析非平稳信号的有力工具，在具有许多优良特性的同时，存在严重的交叉干扰项。在Wigner-Ville分布及Cohen类时频分布具有固定核函数的基础上，研究了基于信号的核函数优化设计的两种方法，径向高斯核函数和最优相位核函数的设计方法。基于信号的核函数的时频表示可以有效地抑制或转移交叉分量，提高时频表示的可读估计，改善其主要性能。     

多Chirp信号的Cohen类时频分布的性能分析

以广泛出现在工程应用和许多物理现象中的多分量Chirp信号为对象,研究了Cohen类时频分布对这种信号时频表示性能,定量地描述了各种时频分布的时频聚集程度,分析了交叉项的特点和抑制机理,提出了现有的Cohen类时频分布对该类信号交叉项抑制存在局限,仿真结果验证了理论分析的正确性.     

Cohen类双线性时频分布在语音识别上的应用

短时分析技术有着与生俱来的短时平稳假设限制,众多非平稳信号处理技术有着克服这一根本技术限制的潜力。非平稳信号处理技术中的Cohen类双线性时频分布技术拥有良好的时频分辨率,其中的WVD的时频分辨率已达到不确定原理下界,在非平稳信号处理技术中有独特优势。详细介绍了将这一优势在语音自动识别上的应用原理,提出了一种新型的语音智能识别方法。     

基于时频分布盲辨识算法的电磁干扰信号分析

提出一种可用于分离不同时频分布的非平稳信号的盲信号辨识算法。采用Wigner－Ville分布（WVD）进行盲源分离时,合成信号有交叉项存在,其分离性能不理想。而Cohen 类时频分布可以抑制交叉项,并且保持时频聚集性。因此,在TFBSS 中,Cohen 类时频分布可以取得更好的分离性能。分析了Cohen 类时频分布对交叉项的抑制性能,以及对盲源分离性能的影响,结果表明：采用盲辨识算法进行电磁干扰信号分离,其效果明显优于采用WVD进行分离的效果。     

多维非平稳信号的时频分析方法研究

时频分析方法能够同时描述信号在时间和频率域的能量密度与分布情况,为非平稳信号处理提供了有力的工具。文中对非平稳信号的双线性时频分析方法进行了讨论,分析了几种固定核函数的Cohen类分布在典型非平稳信号时频分析中的优缺点;详细讨论并实现了多维信号的Cohen类分布,比较了几种固定核函数的Cohen类分布分析多维信号时在抑制交叉项、自项分辨率保持方面的优缺点与核函数参数选择问题。     

典型非线性固定核函数时频分布的性能分析

传统的傅立叶变换无法满足对非线性、非平稳信号的分析,时频分析旨在构造一种时间和频率的密度函数,能够反映非平稳信号的时变特征,是非平稳信号分析的有力工具。文章讨论了魏格纳-威尔分布、伪平滑魏格纳-威尔分布及乔伊-威廉斯分布三种固定核函数时频分析方法的性能特征,进行计算机仿真,最后总结这三种分布在时频分辨率、抗干扰能力和信号适用性方面的优缺点。     

一种新的抑制交叉项的时-频分布的分析

最近提出了一种新的时频分布，在保持高时频分辨率的同时可抑制交叉项，本文从信号模糊域滤波的角度分析了该分布中核函数的设计方法与思路，并针对多分量线性调频信号用该分布进行了计算机仿真。仿真结果证明了该分布在抑制交叉项并保持较高时频分辨率方面的有效性。     

基于小波的广义时频分布及其与Cohen类的关系

STFT, WT, WVD和Cohen类是目前信号分析和处理的有力工具。本文用WT的二次型时频形式分析了线性时频特性,研究了尺度图和变化窗谱图与Cohen类之间的关系;将尺度图纳入到Cohen类的框架,从而使WT广义化为时频域、时延频偏域的双线性时频分布以及谱相关域的二维频率分布;定义了尺度图、小波模糊函数(WAF)和小波谱相关函数(WSCF);分析了它们的物理意义。推导并仿真了单频、双频和高斯白噪声的WSCF,分析了各自的特性。最后通过引入一种离散小波变换的加密算法,解决了小波时频分布的计算问题。     

A NEW QUADRATIC TIME-FREQUENCY DISTRIBUTIONAND A COMPARATIVE STUDY OF SEVERAL POPULARQUADRATIC TIME-FREQUENCY DISTRIBUTIONS

A new quadratic time-frequency distribution (TFD) with a compound kernel is proposed and a comparative study of several popular quadratic TFD is carried out. It is shown that the new TFD with compound kernel has stronger ability than the exponential distribution (ED) and the cone-shaped kernel distribution (CKD) in reducing cross terms, meanwhile almost not decreasing the time-frequency resolution of ED or CKD.     

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.     

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     

瞬时频率和时频分布

本文首先介绍了瞬时频率(IF)的概念和IF的传统计算方法解析信号法,然后从联合时频分析的角度讨论了IF的物理含义及其与时频分布之间的联系,指出二维时频分布的任务之一是对IF进行估计,并且通过对几种时频分布估计性能的比较说明分布形式的选择对估计效果有较大影响。最后,本文提出并初步论证了自适应旋转投影分解法(AOP)计算IF的有效性。     

A high-resolution quadratic time-frequency distribution formulticomponent signals analysis

The paper introduces a new kernel for the design of a high resolution time-frequency distribution (TFD). We show that this distribution can solve problems that the Wigner-Ville distribution (WVD) or the spectrogram cannot. In particular, the proposed distribution can resolve two close signals in the time-frequency domain that the two other distributions cannot. Moreover, we show that the proposed distribution is more accurate than the WVD and the spectrogram in the estimation of the instantaneous frequency of a stepped FM signal embedded in additive Gaussian noise. Synthetic and real data collected from real-world applications are shown to validate the proposed distribution     

Modified Cohen-Lee time-frequency distributions and instantaneousbandwidth of multicomponent signals

Cohen (1989, 1995) has introduced and extensively studied and developed the concept of the instantaneous bandwidth of a signal. Specifically, instantaneous bandwidth is interpreted as the spread in frequency about the instantaneous frequency, which is itself interpreted as the average frequency at each time. This view stems from a joint time-frequency distribution (TFD) analysis of the signal, where instantaneous frequency and instantaneous bandwidth are taken to be the first two conditional spectral moments, respectively, of the distribution. However, the traditional definition of instantaneous frequency, namely, as the derivative of the phase of the signal, is not consistent with this interpretation, and new definitions have therefore been proposed previously. We show that similar problems arise with the Cohen-Lee (1988, 1989) instantaneous bandwidth of a signal and propose a new formulation for the instantaneous bandwidth that is consistent with its interpretation as the conditional standard deviation in frequency of a TFD. We give the kernel constraints for a distribution to yield this new result, which is a modification of the kernel proposed by Cohen and Lee. These new kernel constraints yield a modified Cohen-Lee TFD whose first two conditional moments are interpretable as the average frequency and bandwidth at each time, respectively     

A centrosymmetric kernel decomposition for time-frequency distribution computation

Time-frequency distributions (TFDs) are bilinear transforms of the signal and, as such, suffer from a high computational complexity. Previous work has shown that one can decompose any TFD in Cohen's class into a weighted sum of spectrograms. This is accomplished by decomposing the kernel of the distribution in terms of an orthogonal set of windows. In this paper, we introduce a mathematical framework for kernel decomposition such that the windows in the decomposition algorithm are not arbitrary and that the resulting decomposition provides a fast algorithm to compute TFDs. Using the centrosymmetric structure of the time-frequency kernels, we introduce a decomposition algorithm such that any TFD associated with a bounded kernel can be written as a weighted sum of cross-spectrograms. The decomposition for several different discrete-time kernels are given, and the performance of the approximation algorithm is illustrated for different types of signals.     

从时频分布到连续子波变换

本文从时频分布的物理概念出发,首先指出了各种时频分析方法的内在联系和差别,然后着重从连续子波变换在信号分析中的物理意义来讨论它的数学表示和特有性质,并和短时傅里叶变换作了比较。接着对连续子波变换在时间尺度平面上的离散化的概念作了扼要描述。最后,基于文中叙述的物理概念,对子波变换的应用和进展作了简要评述。     

A four-parameter atomic decomposition of chirplets

A new four-parameter atomic decomposition of chirplets is developed for compact and precise representation of signals with chirp components. The four-parameter chirplet atom is obtained from the unit Gaussian function by successive applications of scaling, fractional Fourier transform (FRFT), and time-shift and frequency-shift operators. The application of the FRFT operator results in a rotation of the Wigner distribution of the Gaussian in the time-frequency plane by a specified angle. The decomposition is realized by using the matching pursuit algorithm. For this purpose, the four-parameter space is discretized to obtain a small but complete subset in the Hilbert space. A time-frequency distribution (TFD) is developed for clear and readable visualization of the signal components. It is observed that the chirplet decomposition and the related TFD provide more compact and precise representation of signal inner structures compared with the commonly used time-frequency representations