非线性调频信号的自适应时频滤波算法

针对传统的时域或频域滤波算法对非线性调频信号滤波去噪效果不好的问题,本文提出了一种时频域内非线性调频信号的自适应滤波去噪算法。首先对原信号进行广义S变换获得其时频分布,接下来利用有效信号时频分布特性选取时频通域,构造区域滤波算子并去除掉时频通域外的噪声分量的时频分布;然后利用有效信号分量的时频聚集性构造自适应时频滤波算子,对含有随机噪声的有效信号分量进行滤波处理,得到滤波去噪后的信号的时频分布;最后利用广义S逆变换将处理后的时频分布变换到时间域,得到滤波去噪后的信号。通过仿真实验的结果可知,本文提出的算法在非线性调频信号的滤波去噪和有效特性保持方面取得了较好的效果。      

基于能量峰区域时频滤波的信号估计

本文提出了一种基于能量峰时频区域滤波的信号估计方法。首先,设计了一种基于能量阈值的时频区域提取方法,识别出信号在时频面上的能量峰,并提取出能量峰所占据的时频区域;利用线性时频滤波器获取信号中的分量,将这些分量的时频分布叠加得到改善的时频分布。仿真结果表明,对于由时频不相交分量组成的信号,本方法可以分离出其中的信号分量,并能得到较优的时频分布。     

基于时频域联合滤波的中段群目标信号分离

该文针对中段群目标回波信号分离问题,提出了基于时频域联合滤波的信号分离算法.该算法首先基于自相关原理估计出某一目标的微动周期;然后根据所估计周期对回波进行分段,利用S变换提取各段信号在时频域的强能量区域,各段信号强能量区域的交集即对应了某一目标在时频面上的支撑域.基于该支撑域对回波进行时频域联合滤波即可得到某一目标的回波信号,实现回波信号的分离.仿真结果验证了该文所提算法的有效性.      

脉冲噪声下跳频信号时频图修正

为了在α稳定分布噪声的环境下获得清晰的跳频信号时频图,提出一种基于分数低阶SPWVD(Smoothed Pseudo Wigner-Vile Distribution)与形态学滤波相结合的跳频信号时频图修正算法。首先,根据接收到的多跳频信号建立跳频信号的模型和α稳定分布噪声模型;然后,采用低阶SPWVD变换抑制时频图中脉冲噪声;最后,根据形态学滤波处理方法对残留噪声进一步抑制进而得到清晰时频图。理论分析和仿真结果表明,所提算法在广义信噪比为-5 dB时仍可以得到清晰可靠的跳频信号时频图,并且基于时频图的参数估计性能优良。     

混合噪声下基于Viterbi同步压缩S变换的FM信号分析

该文针对同步压缩S变换(SSST)在混合噪声下的失真问题,提出一种新型稳健性广义同步压缩S变换(GSST)。该方法首先改进Viterbi算法以提高S变换在混合噪声下的时频分析性能,在获取调频(FM)信号的相位轨迹信息后,利用同步压缩技术提高时频聚集性。仿真实验表明,在α-高斯混合噪声环境下,该方法能够在低信噪比下精确获取FM信号的时频信息,有效改善了传统同步压缩算法的稳健性和适用性。     

Time-frequency projection filters and time-frequency signalexpansions

We consider the problems of designing a linear, time-varying filter with a specified “time-frequency (TF) pass region” and of constructing an orthonormal basis for the parsimonious expansion of signals located in a given TF support region. These problems of TF filtering and TF signal expansion are reduced to the problem of designing a “TF subspace”, i.e., a linear signal space comprising all signals located in a given TF legion. Specifically, the TF filter is taken to be the orthogonal projection operator on the TF subspace. We present an optimum design of TF subspaces that is based on the Wigner distribution of a linear signal space and is an extension of the well-known signal synthesis problem. The optimum TF subspace is shown to be an “eigenspace” of the TF region, and some properties of eigenspaces are discussed. The performance of TF projection filters and TF signal expansions is studied both analytically and via computer simulation     

结合时变滤波和时频掩码的语音增强方法

本文提出了一种结合鲁棒时变滤波和时频掩码的语音增强方法。首先在带噪语音的时频域中,结合图像处理方法估计出初始瞬时频率信息。然后基于该瞬时频率信息,利用鲁棒时变滤波算法构建降噪后的语音信号。最后根据重构语音的时频特征预测时频掩码。该掩码在带噪语音的时频域中能够有效地保留语音成分且抑制噪声成分,从而达到语音增强的目的。实验结果表明,在几种常见背景噪声环境下,所提语音增强算法在抑制背景噪声干扰、提升语音整体质量方面表现良好,尤其是在低信噪比环境下具有明显的优势。      

On generalized-marginal time-frequency distributions

We introduce a family of time-frequency (TF) distributions with generalized marginals, i.e., beyond the time-domain and the frequency-domain marginals, in the sense that the projections of a TF distribution along one or more angles are equal to the magnitude squared of the fractional Fourier transforms of the signal. We present a necessary and sufficient condition for a TF distribution in Cohen's class to satisfy generalized marginals. We then modify the existing well-known TF distributions in Cohen's class, such as Choi-Williams (1989) and Page distributions, so that the modified ones have generalized marginals. Numerical examples are presented to show that the proposed TF distributions have the advantages of both Wigner-Ville and other quadratic TF distributions, which only have the conventional marginals. Moreover, they also indicate that the generalized-marginal TF distributions with proper marginals are more robust than the Wigner-Ville and the Choi-Williams distributions when signals contain additive noise     

S-transform applied to laser Doppler flowmetry reactive hyperemia signals

Laser Doppler flowmetry signals give information about many physiological activities of the cardiovascular system. The activities manifest themselves in rhythmic cycles. In order to explore these activities during the reactive hyperemia phenomenon, a novel time-frequency method, called the S-transform, based on a scalable Gaussian wavelet, is applied. The goal is to have a deeper understanding of reactive hyperemia. This paper focuses on the evaluation of the different activities between a rest signal and an hyperemia signal, both acquired simultaneously on the two forearms of healthy subjects. The results show that after the release of the occlusion, the myogenic, neurogenic, and endothelial related activities clearly increase on the forearm where the occlusion took place. Then, they return progressively to their basal level. However, on the rest forearm, no increase is noted for the three activities. The mechanisms that take place during reactive hyperemia are, therefore, local. The S-transform proves to be a suited time-frequency method, in order to analyze laser Doppler signal underlying mechanisms.     

由时频分布引导的四参数子空间匹配追踪算法

为了克服四参数匹配追踪计算量巨大的缺点,本文提出了一种由时频分布引导的四参数子空间匹配追踪算法.该算法由引导时频分布确定chirp原子的时频中心,然后用模板匹配方法搜索原子的尺度和调频率(chirp rate).这样,一个高计算复杂度的四维搜索问题被转化为两个相对简单的二维搜索问题.为有效利用时频分布,每次搜索多个时频原子,这些原子不再相互正交.为此,我们利用最小二乘方法计算信号(或残差信号)在相应子空间上的正交投影.同快速脊追踪算法相比,四参数子空间匹配追踪需要更少的原子逼近信号,对实测语音信号的数值计算也证实了这点.     

基于Wigner分布时频遮隔的信号分解算法

综合特征值分解及Wigner分布时频遮隔提出了一种信号分解算法,并推广应用于其他交叉项抑制时频表示.对于由时频面上互不重叠分量合成的多分量信号,证明了信号分量可与各分量Wigner分布之和的逆Fourier变换的特征值分解相对应;通过阈值法可从抑制交叉项时频表示获得信号时频支撑区域,以此为模板遮隔Wigner分布可减少交叉项并保持自项聚集性,其逆Fourier变换的特征值分解就可实现多分量信号分解.仿真实例分析结果表明了该理论与算法的正确性和实用性.最后分析了算法性能并拓展了其实用范围.     

基于频率模型和时频分析的正弦信号频率高精度估计算法

利用时频分析方法估计信号瞬时频率,在低信噪比条件下估计性能较差,但在时频图中,信号频率的变化趋势具有一定的规律,基本上都是围绕着信号的真实频率。基于此,给出了一种结合时频分析和信号频率模型相结合的方法,以实现信号瞬时频率的高精度估计。利用时频分析具有的良好时频分布的特点,采用最大能量方法（ME）预先估计得到信号的预估计瞬时频率（EIF）;再利用瞬时频率连续性、平滑性的先验信息,建立了信号瞬时频率估计模型,并采用概率最大原理(MP)估计瞬时频率概率最大的统计变化,估计得到预估计瞬时频率的滤波起始点;最后利用卡尔曼滤波和平滑算法对预估计瞬时频率进行滤波和平滑,从而得到信号频率的精确估计。      

利用时频分析的间歇采样干扰对抗方法

为了提高线性调频脉压雷达在复杂电磁环境下的抗干扰能力,提出了基于时频分析的间歇采样干扰识别与抑制算法。针对间歇采样干扰信号在时域上的不连续性,将雷达接收信号通过扩展处理中的混频处理之后再进行时频分析,通过对时频分析结果的处理进而提取一种特征参量,对雷达接收信号中是否存在干扰进行鉴别;同时将扩展处理与时频分析相结合,根据时频分析求解合适的带通滤波器,用以对雷达接收信号扩展处理的结果进行滤波,从而消除干扰。理论推导和计算机仿真结果表明,这种方法能够在合理的信噪比下鉴别出雷达是否受到间歇采样干扰,并且能够进行有效的抑制。      

基于S变换的线性调频信号时频滤波

线性调频(LFM)信号是一种典型的非平稳信号。对非平稳信号的分析和处理不能仅依靠传统的基于傅里叶变换的分析方法,必须采用时域和频域联合的时频分析方法。将S变换(ST)的时频分析方法应用到LFM信号的滤波中,用tST-tIST和fST-fIST 2种算法组合分别实现了对LFM信号的滤波,然后采用均方误差(MSE)的衡量标准,仿真对比了以上2种算法组合的滤波性能。仿真结果表明:fST-fIST算法的滤波误差较大,不适合对LFM信号的滤波;tST-tIST算法的滤波性能较好,适合对LFM信号的滤波。     

The S-Transform and Its Inverses: Side Effects of Discretizing and Filtering

The aim of this paper is to present a study on the potential and limits of the -transform and its inverses. The S-transform is an extension of the short-time Fourier transform with characteristics of the wavelet transform. It is mostly used for time-frequency analyses. Two different inverse S-transforms have been presented in the literature. We explain why the most recent one is an approximation but a very good one. The level of approximation is calculated in this paper. We then discuss the relative merits of both inverses. A careful study enables us to show that, although both inverses are nearly exact in the infinite continuous domain, this is not true anymore in the practical finite discrete domain. Side effects are quantified, and typical examples are given. Time-frequency filtering is one of the main applications of the S-transform. We evaluate the effects that occur when using the S-transform and its inverses for filtering.     

Tomography time-frequency transform

The paper shows that the fractional Fourier transform (FRFT) of a signal is the Radon transform of the time-frequency distribution of the same signal. Therefore, a time-frequency distribution known as the tomography time-frequency transform (TTFT) is defined as the inverse Radon transform of the FRFT of the signal. Because the computation of the TTFT does not explicitly require any window or kernel function, high resolutions in both the frequency and time domains can be achieved. When the signal contains multiple components, the cross terms can be effectively removed by an adaptive filtering process that is applied on the FRFT rather than the final result. Therefore, distortions made by the filtering process on the desired signal components can be minimized     

基于时频矩阵局部对比度的跳频信号参数估计

为了提高低信噪比下跳频信号参数估计性能,提出了一种基于时频矩阵局部对比度的跳频信号参数估计算法。根据跳频信号和噪声的时频分布不同特点,利用时频矩阵在不同尺度滑窗下的局部能量对比值均值,得到多尺度局部能量对比特征矩阵,通过此特征矩阵和自适应阈值分离得到仅保留了跳频信号时频信息的时频矩阵P。然后从P中提取时频跳变信息,精确估计跳频信号的跳频周期、起跳时间和跳变频率。仿真结果表明,与传统局部对比度（local contrast measure,LCM）法及形态学滤波法相比,本文算法具有更好的跳频信号提取效果和更高的参数估计精度,其有效性与实用性在DSP+FPGA的硬件系统上得到了测试验证。     

基于自相关和时频分析的跳频参数估计

为解决低信噪比条件下跳频参数估计算法性能低的问题,提出了一种基于自相关和时频分析的跳频参数估计算法。首先,采用基于能量检测的分段自相关算法对接收端信号进行预处理;然后,进行时频变换,得到信号的时频矩阵,通过二值化和形态学滤波完成对信号的降噪提取;最后,通过聚类算法完成参数估计。仿真实验表明,该算法具有较高的估计精度和良好的抗噪声性能,在信噪比最低为-11 dB时估计误差数量级仍为10^-7,同时自相关运算对参数估计算法的抗噪声性能具有明显的提高作用。     

基于时频分布的多分量LFM信号检测

采用二次型时频分析方法对多分量线性调频信号进行分析,获取信号的时频域能量分布。首先讨论了线性调频信号的两种二次型时频分布：魏格纳-威尔分布及其改进形式——重排魏格纳-威尔分布;在此基础上,进一步对比验证了霍夫变换和若当变换对线性调频信号时频域能量的积累效果,以实现噪声背景中的多分量线性调频信号检测。仿真结果显示基于二次型时频分布的处理是实现多分量线性调频信号检测的一个有效方法。     

一种基于SPWVD-WVD的高质量时频分析方法及ISAR成像应用

交叉项干扰抑制与高时频聚集度是准确反应信号的时频分布特征的重要因素。传统的魏格纳-维尔分布(Wigner-Ville Distribution,WVD)算法虽能获得较高的时频分辨特征,但分析多成分信号时存在严重的交叉项干扰问题,限制了其实用性;而平滑伪魏格纳-维尔分布(Smoothed Pseudo Wigner-Ville Distribution,SPWVD)算法虽在一定程度上抑制交叉项干扰,但降低了时频聚集度。为了解决上述问题,提出了基于SPWVD-WVD的时频分析方法。该方法利用SPWVD与WVD之间的滤波互消效应,将SPWVD二值化结果与WVD结果进行矩阵运算,最终得到高质量的时频分析结果。实验结果表明,所提出的算法能够有效去除多分量信号的交叉项干扰,提高信号分析结果的时频聚集度,还原多分量信号的真实时频分布。最后将该算法成功应用于逆合成孔径雷达成像中。