调制与解调技术、调制解调器

0309782一种新的多线性调频信号时频表示[刊]/李英祥//电子学报.—2002,30(12).—1879～1881(K)本文提出了一种用于多线性调频信号时频表示的方法。首先由 Radon-Ambinguity 变换设计出线状核函数,以去除噪声和多线性调频信号之间的交叉项在模糊域中的影响,再通过二维傅立叶变换得到一种新的时频表示。仿真实验证明了此方法可以有效的去除噪声和多信号之间交叉项的影响,在低信噪比下也十分有效。参4     

基于乘积性模糊函数的核函数设计方法

利用乘积性模糊函数,提出一种双线性时频分布的核函数设计新方法,能够在模糊域有效地抑制噪声和多分量间的交叉项.在多分量强弱信号环境中,提出基于逐次减小误差的递归算法重构强信号,减小传统分析方法的残余信号影响和参数误差传播效应.利用分段近似的原则,可将非线性调频信号分解为多分量线性调频信号,从而将核函数设计方法推广到非线性调频信号环境.最后,仿真结果证实了该方法对多分量调频信号的有效性.     

基于DMFT的LFM信号参数估计

线性调频信号是低截获概率雷达常用的一种信号形式,如何在低信噪比情况下检测线性调频信号一直是人们研究的焦点之一.在离散匹配傅里叶变换的基础上对算法进行改进,并利用改进后的算法分别对单分量和多分量线性调频信号进行仿真,仿真结果表明离散匹配傅里叶变换能够在低信噪比情况下比较准确地估计出线性调频信号的参数,不存在交叉项问题.离散匹配傅里叶变换是一种针对线性调频信号有效的参数估计方法.     

基于乘积性模糊函数的多线性调频信号时频分布

本文提出一种新的多分量线性调频信号的双线性时频分布.在核函数设计中,提出了两种基于乘积性模糊函数的方法.两种方法在信号的模糊域能够有效地滤出噪声和交叉项,并保留绝大部分的自项能量,得到准确的核函数.仿真结果证实了该方法能够有效地抑制噪声和交叉项,提高时频分辨率,同时可以适应低信噪比环境.     

多分量线性调频信号的Wigner-Ville分布交叉项去除

针对多分量线性调频信号WVD（Wigner-Ville Distribution）检测中的交叉项问题,提出一种交叉项去除方法.利用自项和交叉项的频率特性,将WVD时频矩阵进行坐标旋转,在变换域上滤波去除交叉项.理论推导了矩阵旋转变换公式以及旋转后自项和交叉项的表达式,并且针对缓慢震荡的交叉项受滤波器性能限制不能完全去除的情况,提出能量加权的方法进行改进.仿真和实验结果验证,该方法不仅能够去除交叉项,且不会降低分辨率.     

STTFD的特性分析及其在线性调频信号 参数估计中的应用

 精确地估计线性调频(LFM)信号的中心频率和调频率值,在雷达信号处理中有着十分重要的作用.WVD变换具有估计线性调频信号的中心频率和调频率特性,但当存在多个线性调频分量时,交叉项会严重影响线性调频信号中心频率和调频率的估计.针对这一问题,本文提出了基于STTFD(时频分布尺度变换)的线性调频信号参数估计的方法,即在时频平面内对信号进行尺度变换,去除信号瞬时时间和相关函数延迟量的耦合,使线性调频信号的频率随时间变化呈线性分布变为不随时间变化的常数分布,再通过在时频平面内沿时间轴做FFT积累所得到的信号峰值位置来估计信号的中心频率和调频率参数值,并且对STFD的特性以及相关函数延迟量和尺度变换常数对STTFD的影响进行了详细的分析.在本文最后,通过仿真数据验证了STTFD的特性及其精确估计线性调频信号参数的有效性.     

脉冲噪声下基于压缩变换函数的LFM信号参数估计

针对现有线性调频(LFM)信号参数估计方法在脉冲噪声下性能退化甚至完全失效的问题,该文提出一种脉冲噪声下估计LFM信号参数的新方法.该文构造了一种新的压缩变换(CT)函数,分析了该函数在零点附近的近线性,推导了任意随机变量经该函数变换后的2阶矩有界,证明了函数变换前后LFM信号的初始频率和调频斜率信息不变.将经过函数变换后的信号进行分数阶傅里叶变换(FrFT),根据FrFT域中峰值坐标和信号参数的关系,寻找变换域中的峰值点,实现信号参数的估计.仿真实验表明,该方法可有效抑制脉冲噪声且能准确估计出信号的参数信息,实现简单,不需要噪声的先验信息,具有良好的稳健性.     

用于多分量线性调频信号的自适应核分布分析

该文针对多分量线性调频信号,提出了一种新的自适应核时频分布-自适应高斯核分布,并给出了有效的核函数估计准则;以自适应高斯核分布为例,分析了采用自适应核时频分布对信号自身项及交叉项的影响,从而说明自适应核相对于固定核的优势所在;总结了基于模糊域自适应设计多分量线性调频信号核函数的一般方法。计算机仿真结果表明了自适应高斯核分布在抑制交叉项并保持较高时频分辨力方面的有效性。     

线性调频信号分数阶频谱特征分析

线性调频信号是一种典型的非平稳信号,广泛应用于雷达、声纳、通信等领域.分数阶Fourier变换是一种新兴的时频变换,由于其独特的性质,成为线性调频信号检测与参数估计的一种良好工具.尤其是,作为一种线性变换,分数阶Fourier变换在处理多分量线性调频信号时能够避免交叉项的干扰.但是,多分量线性调频信号在分数阶Fourier域也存在相互影响的问题.为了分析该问题,研究线性调频信号在分数阶Fourier域的频谱分布特征是非常必要的.本文根据分数阶Fourier变换的定义以及分数阶Fourier变换与时频分布的关系,分析了线性调频信号在分数阶Fourier域的频谱分布特征,以及线性调频信号的分数阶频谱分布与分数阶旋转角α的变化关系;根据离散分数阶Fourier变换的实现算法,讨论了线性调频信号在离散分数阶Fourier变换条件下的分数阶频谱的分布特征,以及线性调频信号在分数阶Fourier域的能量谱的近似表达式.最后,利用LFM信号的分数阶频谱的分布特征,分析了多分量LFM信号中的信号尖峰偏移问题,并给出信号尖峰发生偏移的条件.本文为定量分析分数阶Fourier域多分量线性调频信号之间的相互影响奠定了基础,为改善分数阶Fourier变换对多分量线性调频信号的处理能力提供了参考.     

复杂噪声环境下基于LVD的LFM信号参数估计

针对传统的线性调频(LFM)信号参数估计方法平滑交叉项时,会出现参数估计精度降低和计算复杂度增加等问题,该文引入LVD(LVs Distribution)方法,该方法可以在参数空间直接显示中心频率和调频斜率(CFCR)。LVD首先对对称参数瞬时自相关函数(PSIAF)进行尺度变换,消除信号在时间轴上的线性频率偏移,然后对尺度变换后的时间变量作2维傅里叶变换,将1维LFM信号转化为2维单频信号。信号各分量在LVD平面表现为多个独立尖峰,使交叉项的能量聚集影响可忽略不计,且信号各峰值所在位置对应于各信号分量的中心频率和调频斜率。LVD可有效抑制高斯噪声,但在脉冲性较强的稳定分布噪声中,该方法在CFCR域的性能退化甚至失效。对此,该文结合分数低阶统计量理论,提出一种稳定分布噪声环境下的分数低阶LVD新方法。仿真实验表明该方法在高斯噪声和脉冲噪声环境下均可稳定工作,具有较好的鲁棒性。     

Analysis of Multi-Component Non-Stationary Signals Using Fourier-Bessel Series and Wigner-Hough Transform

We presented a novel Fourier-Bessel (FB) series and Wigner-Hough transform (WHT) method for the analysis of multi-component non-stationary signals. The FB series decomposed multi-component non-stationary signals into mono-component signals. The Wigner-Ville distribution (WVD) was applied to each mono-component signal to analyze its time-frequency distribution (TFD). Summing up the WVDs of the individual components resulted in TFDs of the multi-component signals, where the cross terms and noise were significantly reduced. The Hough transform (HT) was applied on the TFD of the multi-component signal (obtained from FB-WVD). The HT provides an important tool for mapping the signals onto a parameter space where the detection and estimation problems are made easier. This mapping can be used in the detection and parameter estimation of signals which are unknown and embedded in noise.     

Pitch-synchronous wavelet representations of speech and musicsignals

A new wavelet representation is explored. The transform is based on a pitch-synchronous vector representation and it adapts to the oscillatory or aperiodic characteristics of signals. Pseudo-periodic signals are represented in terms of an asymptotically periodic trend and aperiodic fluctuations at several scales. The transform reverts to the ordinary wavelet transform over totally aperiodic signal segments. The pitch-synchronous wavelet transform is particularly suitable to the analysis, rate-reduction coding and synthesis of speech signals and it may serve as a preprocessing block in automatic speech recognition systems. Feature extraction such as separation of voice from noise in voiced consonants is easily performed by means of partial wavelet expansions. A stochastic model of aperiodic fluctuations is proposed     

低信噪比下频率编码信号参数估计

针对低信噪比条件下,频率编码信号的瞬时频率估计容易受噪声影响的问题,提出了将自适应阈值消噪技术与小波变换结合起来的新方法。首先利用连续小波变换计算信号的小波谱,然后对小波谱进行自适应阈值消噪处理,进而提取小波谱脊线,得到频率编码信号的脉内调制信息。计算机仿真结果验证了该方法的可行性。     

Wigner—Ville分布和模糊函数对多个LFM信号检测性能分析

现代雷达系统中,线性调频(LFM)信号是一种常用的脉冲压缩雷达信号。文章对LFM信号的Wigner-Ville分布进行Wigner-Hough变换得到Wigner-Ville分布图(WVD),并对模糊函数进行Wigner-Hough变换得到模糊图。通过数字仿真对比得出模糊函数需要取绝对值才能利用Hough变换检测直线,受交叉项影响大一些。而Wigner-Ville分布函数可以直接利用Hough变换检测直线,交叉项得到抑制,检测效果好,验证了分析的结论。该文的研究对LFM信号检测性能分析有一定的指导意义。     

Adaptive time-varying cancellation of wideband interferences inspread-spectrum communications based on time-frequencydistributions

The paper proposes an adaptive method for suppressing wideband interferences in spread-spectrum (SS) communications. The proposed method is based on the time-frequency representation of the received signal from which the parameters of an adaptive time-varying interference excision filter are estimated. The approach is based on the generalized Wigner-Hough transform as an effective way to estimate the instantaneous frequency of parametric signals embedded in noise. The performance of the proposed approach is evaluated in the presence of linear and sinusoidal FM interferences plus white Gaussian noise in terms of the SNR improvement factor and bit error rate (BER)     

Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms

In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. We use the denoising capabilities of decimated and undecimated multiwavelet transforms, DMWT and UMWT respectively, for the removal of noise from microarray data. Multiwavelet transforms, with appropriate initialization, provide sparser representation of signals than wavelet transforms so that their difference from noise can be clearly identified. Also, the redundancy of the UMWT transform is particularly useful in image denoising in order to capture the salient features such as noise or transients. We compare this method with the discrete and stationary wavelet transforms, denoted by DWT and SWT, respectively, and the Wiener filter for denoising microarray images. Results show enhanced image quality using the proposed approach, especially in the undecimated case in which the results are comparable and often outperform that of the stationary wavelet transform. Both multiwavelet transforms outperform the DWT and the Wiener filter.     

基于CZT的多子带信号时差估计方法

针对稀疏多子带信号多子带综合时差估计难,高分辨时差估计计算量大的问题,提出了一种基于线性调频Z变换(CZT)的稀疏多子带信号融合的快速高分辨时差估计算法。该方法通过CZT对各子带信号的互谱进行高分辨的谱分析得到各子带信号的高分辨互相关函数,通过分析各子带互相关函数的相位关系实现各子带互相关函数的相参积累。相比传统广义互相关算法,该方法在实现多子带相参积累的同时,大大降低了计算量。仿真结果表明:提出的基于CZT的多子带融合快速时差估计方法能精确地估计时差,相比单子带信号的时差估计性能可以获得明显提升。     

Two-branch wavelet denoising for accurate spectrum sensing in cognitive radios

In order to detect the unused spectrum bands (the spectrum holes) efficiently in cognitive radios with low signal-to-noise radio (SNR), we propose to adopt two independent branches of wavelet to detect the singularities of the received signals’ power spectrum density (PSD). The sensing structure is flexible such that we can use one or two branches to cope with different SNRs. Under low SNR condition, each branch uses distinct characteristics between noise and signals in the wavelet transform to eliminate the singularities generated by the noise. By using bandpass filter to calculate PSD values of the subbands which are distinguished by the signal’s singularities, the subband with the minimum PSD value among all of the subbands could be found. Then, the results of the two branches are merged and analyzed in order to make the final decision. Finally, we use signal reconstruction to further remove the noise and then accurately detect the spectrum holes. When the SNR is high, only one branch through the denoising procedure is needed to get accurate sensing result. Our simulation results show that the two-branch wavelet method is more accurate than conventional approaches under given SNRs.