超宽带脉冲信号波形失真仿真与分析

超宽带无线信道的频率依赖特性直接影响到超宽带接收机的设计和以脉冲波形为基础的多址技术。本文针对频率依赖特性对超宽带脉冲信号波形产生的影响,分别从相位谱和幅度谱两个方面分析超宽带脉冲信号的相位失真和幅频失真特性,建立了描述脉冲波形失真特性的数学模型。使用该模型对高斯二阶导数脉冲信号的波形失真进行仿真分析,得到了只发生相位响应失真、只发生幅度响应失真、以及同时发生相位—幅度响应失真的脉冲波形。     

对角切片谱法分析信号中相位耦合的频率分量

给出了三阶累积量对角切片谱的性质，分析了水中目标辐射信号的特点，并将3阶累积量对角切片谱法应用于提取水下目标辐射信号中相位耦合的频率分量。以水下某目标辐射噪声的实测数据为例，进行了仿真研究。结果表明，该方法在提取水下运动目标辐射信号中的参加相位耦合的频率分量和相位耦合产生的频率分量、抑制环境噪声和非耦合的频率分量等方面的性能优于自相关谱法。     

基于分数低阶统计量的双谱及其估计方法

稳定分布可更好地描述实际中所遇到的具有显著脉冲特性的随机噪声.为了更好地抑制信号背景中的非高斯噪声,本文提出了基于分数低阶的双谱定义,并给出在分数低阶有色噪声背景下双谱非参数和参数模型的估计方法.仿真结果表明,同传统的双谱估计相比较,非参数法分数低阶双谱估计能有效的识别信号,保留了信号的幅度和相位信息,但存在较大的估计方差.基于AR模型的分数低阶双谱估计具有最大的谱平坦度,能够有效地抑制噪声,具有良好的韧性.     

基于三阶累积量对角切片的直接自适应谱线增强算法

依据随机平方相位耦合信号三阶累积量不为零的特点，提出了从高斯噪声环境中恢复平方相位耦合信号的基于三阶累积量对角切片的直接自适应谱线增强算法。该算法恢复平方相位耦合信号、抑制高斯噪声的性能优于传统的自适应谱线增强算法。仿真结果证实了这种算法的有效性。     

一种改进的基于支持向量机的OFDM识别算法

针对OFDM信号与单载波信号调制识别,提出了一种基于高阶累积量特征的改进方法.通过分析复信号幅值的归一化四阶累积量特性,以及信号的瞬时频率和功率谱特征,改进和提出新的特征参数,采用支持向量机分类器,实现了AWGN信道下包括OFDM在内的9种信号的制式自动识别.该方法具有特征参数易于提取、抗噪性好、识别准确率高的优点.利用MATLAB仿真证明在信噪比不小于7dB的情况下,OFDM信号的识别准确率达99%.     

基于椭圆球面波信号的连续相位调制及其频谱性能分析

椭圆球面波信号（PSWF）是时频域最佳能量聚集性信号,探索性地将PSWF信号应用于连续相位调制（CPM）技术,对基于椭圆球面波信号的CPM调制信号产生过程进行了描述;给出了采用自相关函数法对基于PSWF的CPM调制信号功率谱密度计算方法;结合数值计算、分析对比了最小频移键控、正弦频移键控、高斯最小频移键控以及基于高斯函数的CPM调制信号功率谱密度及其信号占用带宽等特性。数值计算结果表明椭圆球面波信号相比矩形脉冲、升余弦脉冲、高斯脉冲及（类）高斯脉冲作为CPM的基带调频信号,可以获得频谱性能、能量聚集性较好的CPM信号。     

基于谱域联合特征的信号调制类型识别

为了在低信噪比条件下识别信号的调制类型,提出了一种由信号瞬时包络的高阶组合矩、零中频信号的高阶组合累积量和谱相关函数共同构建的联合特征,用于数字调制信号调制类型识别的方法。新方法分析了多种常规数字调制信号的瞬时包络的幅度分布特性,利用了高斯过程的高阶累积量当阶次大于2时恒等于零的特性和谱相关函数突出周期平稳信号的特性,对高斯噪声具有较好的抑制作用。仿真试验结果表明,该方法运算简单,适应性强,在低信噪比下正确识别率高。     

基于四阶累积量特征分解的空间谱估计测向方法

传统的空间谱估计测向方法大都是在二阶统计基础上提出的。而高阶累积量(cumulant)及其对应的高阶谱具有更加丰富的信息,而且对任何谱特性的高斯噪声都有很好的抑制能力。本文研究了基于四阶累积量特征结构分析的谱估计测向方法。从累积量的基本定义和性质出发,导出了基于特征分解的测向算法,并给出了模拟实验和测向系统外场实验结果。实验结果显示,基于四阶累积量方法的性能优于传统谱估计测向方法,特别是对于未知谱特性的色高斯噪声的情况。     

一种雷达辐射源双谱二次特征提取方法

针对常规参数进行复杂体制雷达辐射源信号分选时存在的问题,对信号双谱图分别进行频率分区和奇异值分解,二次提取其双谱分布熵和奇异谱熵作为雷达辐射源特征参数.该方法利用双谱分析可以完全抑制高斯有色噪声对信号的影响,同时保留信号的幅度和相位信息的特点,并有机地融合了双谱理论、奇异值分解和信息熵理论的各自优点,反映出信号的本质信息.采用模糊C均值聚类算法对不同信噪比条件下6种典型调制类型的雷达辐射源信号进行聚类分选实验.实验结果表明,该方法取得了较好的分选效果,克服了传统图像特征提取算法特征维数过高和聚集性差的缺点,验证了该方法的有效性.     

基于相位信息的非均匀包络未知脉冲边沿估计方法

对脉冲边沿的估计一直是雷达、电子侦察等领域的重要课题,其结果直接影响雷达特征的提取。本文针对接收脉冲未知且包络非均匀、有剧烈起伏的情况,提出了一种基于相位信息的未知脉冲边沿估计方法。该方法利用信号和噪声相位的统计特性不同,通过求取接收信号相位的标准差将信号段和噪声段区分开来,从而得到脉冲边沿的粗估计;然后在粗估计范围内对信号段进行滑窗检测,进而得到脉冲边沿的精确估计。理论分析和仿真结果表明,当脉冲包络起伏剧烈时,该算法可以有效解决传统脉冲边沿估计方法存在的脉冲分裂问题,且具有运算量小,精度较高的特点。      

Bispectrum estimation: A digital signal processing framework

It is the purpose of this tutorial paper to place bispectrum estimation in a digital signal processing framework in order to aid engineers in grasping the utility of the available bispectrum estimation techniques, to discuss application problems that can directly benefit from the use of the bispectrum, and to motivate research in this area. Three general reasons are behind the use of bispectrum in signal processing and are addressed in the paper: to extract information due to deviations from normality, to estimate the phase of parametric signals, and to detect and characterize the properties of nonlinear mechanisms that generate time series.     

Bispectrum Reconstruction Method of UWB Echoes

Estimation of transient signal in additive noise is very important in radar object detection and recognition. This paper presents a new method for transient signal reconstruction based on bispectrum estimation techniques. The third-order cumulants of the received noisy ultra-wide band echoes are acquired first and an ARMA model is then fitted. The bispectrum of the output signal of the ARMA model will be used to reconstruct the transient signal. Simulation results show that the effect is very good even in lower signal-to-noise (SNR) situation.     

Signal reconstruction from the phase of the bispectrum

The authors present a simple procedure, the bispectrum signal reconstruction (BSR) algorithm, to recover the Fourier phase of a signal from the phase of its bispectrum. By simple analogy, a procedure that recovers the Fourier magnitude of a signal from the magnitude of its bispectrum is also presented. In addition, the authors propose an iterative scheme, the bicepstrum iterative reconstruction algorithm (BIRA), for the reconstruction of a finite impulse response (FIR) sequence from only the phase of its bispectrum, and they demonstrate how some a priori information on the energy of the cepstra coefficients can improve significantly the convergence rate of the algorithm. Both schemes are based on the key observation that the differences of the bispectrum coefficients contain all the information concerning the Fourier phase of the signal, whereas their sums contain the Fourier-magnitude information     

基于双谱理论的雷达辐射源指纹特征提取

针对雷达辐射源指纹特征提取困难的问题,提出了基于双谱切片法和双谱围线积分法,提取辐射源指纹特征的方法。介绍了二相编码信号的无意调制,仿真了理想情况和含有相位噪声的二相编码信号的时域波形。阐述了含相位噪声雷达信号的双谱估计,仿真了含相位噪声的二相编码信号的双谱图及等高图。最后利用双谱切片法和双谱围线积分法,提取了辐射源的指纹特征,通过计算机仿真实验验证了该方法的可实施性。     

Blind separation of impulsive noise from corrupted audio signal

The authors present a new method for separating the impulsive noise from a corrupted audio signal. It is shown that if the audio signal is assumed to be Gaussian distributed, the third-order cumulants of the impulsive noise can be separated, based upon the corrupted audio signal. The impulsive noise can then be reconstructed via the relationship between the bispectrum and the Fourier spectrum. Finally, the audio signal is restored by simply subtracting the reconstructed impulsive noise from the signal     

Detection of quadratic phase coupling from human EEG signals using higher order statistics and spectra

Interactions among neural signals in different frequency components have become a focus of strong interest in biomedical signal processing. The bispectrum is a method to detect the presence of quadratic phase coupling (QPC) between different frequency bands in a signal. The traditional way to quantify phase coupling is by means of the bicoherence index (BCI), which is essentially a normalized bispectrum. The main disadvantage of the BCI is that the determination of significant QPC becomes compromised with noise. To mitigate this problem, a statistical approach that combines the bispectrum with an improved surrogate data method to determine the statistical significance of the phase coupling is introduced. The method was first tested on two simulation examples. It was then applied to the human EEG signal that has been recorded from the scalp using international 10–20 electrodes system. The frequency domain method, based on normalized spectrum and bispectrum, describes frequency interactions associated with nonlinearities occurring in the observed EEG.     

Time delay estimation using the cross bispectrum

The cross bispectrum phase can be effectively used to estimate the time required for a nonGaussian signal to propagate between a pair of spatially separated sensors in the presence of highly correlated Gaussian noise. The authors present a consistent estimator of the phase of the cross bispectrum, derive the exact distribution of the phase of a complex Gaussian sample bispectrum, and show that in most cases the exact distribution can be approximated by a Gaussian distribution. Using this Gaussian approximation, the authors derive the variance of the time delay estimate computed from the sample cross bispectrum of a signal in additive correlated noise. These results allow the performance of time delay estimators based on the cross bispectrum phase to be quantified as a function of the sample size, the skewness of the signal, the signal-to-noise ratio (SNR), and the noise correlation     

Matrix-based higher order spectral analysis for three-wave couplingprocesses

Composite linear and quadratic systems produce three-wave coupling when stimulated by random phase input sinusoids. Due to the nonlinearity of the system, the output frequencies are arithmetically related to the input. Using third-order cumulant statistics and their associated bispectrum, techniques are devised based on phase-insensitive matrix structures for detection and frequency estimation of coupling frequencies. The separation of the third-order cumulant series into symmetric and skew-symmetric portions allows one to exploit their characteristic eigendecompositions for frequency estimation. After symmetrization, biphases can be easily extracted as coefficients of the cumulant sequence. Using a generalized eigenvector representation, one can relate symmetric and skew-symmetric bases by a subspace rotation algorithm. Biphases can be estimated directly from generalized eigenvalues of the matrix pencil formed by symmetric and skew-symmetric matrices. The dimensionality of the matrices can be reduced through the use of cumulant projections that yield a slice of the bispectrum. The Radon transform procedure is related to bispectral processing through an isotropic radial-slice Volterra filter. The compact third-order Kronecker product matrix formulation and algorithms for coupling frequency estimation can also be converted for use in biphase estimation. Simulations showing the performance of the above procedures are also presented for both synthetic and biomedical time series     

Performance Study of Adaptive Filtering in Bispectrum Signal Reconstruction

The problem of reconstructing a signal waveform when the observed realizations are corrupted by intensive noise and random shifts is considered in this paper. Several ways of performing bispectrum filtering are proposed and investigated. First, it is shown that the signal reconstruction is more efficient if one applies smoothing to the recovered real and imaginary parts of the Fourier spectrum separately instead of filtering the magnitude and phase spectra recovered from a bispectrum estimate. Second, several nonadaptive filters are studied, and it is demonstrated that the proper choice of the filter type and its parameters is critical. Some adaptive filtering techniques based on the Z-parameter and on local polynomial approximation (LPA)-intersection of confidence intervals (ICI) are discussed. The performances of nonadaptive and adaptive filtering techniques in the bispectrum-based signal reconstruction are studied using the mean-squared error as the criterion. It is shown that the use of LPA-ICI and other adaptive filters provides improvement of signal reconstruction in comparison to the conventional bispectrum method and the combined bispectrum filtering methods proposed earlier for nonadaptive filters. The benefits achieved are mainly observed for low (smaller than unity) signal-to-noise ratios.     

基于复Morlet小波相位的红外调制热像检测方法

为了提高红外调制热像法对复合材料缺陷的检测能力,研究了利用基于复Morlet小波相位分析的红外热像序列处理方法。依据热激励调制频率和红外图像采样频率,自适应地选择小波变换尺度总数,然后利用复Morlet小波系数相位从原始热像序列重建相位图序列。对含有分层缺陷的玻璃纤维增强复合材料参考试件进行了调制热像检测实验,以信噪比为评价指标对该方法的实际处理效果进行了定量评定,并与傅里叶变换法进行了对比。结果表明:复Morlet小波相位法可以分离调制热像信号的低频和高频噪声,能稳定地提取试件不同深度的分层缺陷,对热激励调制频率的变化有良好宽容性,因此是调制热像法的有效数据处理方法。