Speech-stream detection in short-wave channel based on empirical mode decomposition and higher-order statistics

To capture the presence of speech embedded in nonspeech events and background noise in shortwave non-cooperative communication, an algorithm for speech-stream detection in noisy environments is presented based on Empirical Mode Decomposition （EMD） and statistical properties of higher-order cumulants of speech signals. With the EMD, the noise signals can be decomposed into different numbers of IMFs. Then, the fourth-order cumulant （ FOC ） can be used to extract the desired feature of statistical properties for IMF components. Since the higher-order eumulants are blind for Gaussian signals, the proposed method is especially effective regarding the problem of speech-stream detection, where the speech signal is distorted by Gaussian noise. With the self-adaptive decomposition by EMD, the proposed method can also work well for non-Gaussian noise. The experiments show that the proposed algorithm can suppress different noise types with different SNRs, and the algorithm is robust in real signal tests.

Higher-Order Statistics and Nonlinear Processes in Space Plasmas

Statistics of order 2 (variance, auto and cross-correlation functions, auto and cross-power spectra) and 3 (skewness, auto and cross-bicorrelation functions, auto and cross-bispectra) are used to analyze the wave-particle interaction in space plasmas. The signals considered here are medium scale electron density irregularities and ELF/ULF electrostatic turbulence. Nonlinearities are mainly observed in the ELF range. They are independently pointed out in time series associated with fluctuations in electronic density and in time series associated with the measurement of one electric field component. Peaks in cross-bicorrelation function and in mutual information clearly show that, in well delimited frequency bands, the wave-particle interactions are nonlinear above a certain level of fluctuations. The way the energy is transferred within the frequencies of density fluctuations is indicated by a bi-spectra analysis.

Higher-order nonclassical effects generated by multiple-photon annihilation-then-creation and creation-then-annihilation coherent states

We explore two observable nonclassical properties of quantum states generated by repeatedly operating annihilationthen-creation（AC） and creation-then-annihilation（CA） on the coherent state, respectively, such as higher-order subPoissonian statistics and higher-order squeezing-enhanced effect. The corresponding analytical expressions are derived in detail depending on m. By numerically comparing those quantum properties, it is found that these states above have very different nonclassical properties and nonclassicality is exhibited more strongly after AC operation than after CA operation.

平坦慢衰落信道下基于HOS的PSK调制盲信道估计

提出一种基于符号高阶统计量(HOS,high-order statistics)的MPSK调制信道衰落系数盲估计算法。针对平坦慢衰落信道模型,首先分析了MPSK调制符号高阶统计量特征,证明了MPSK调制符号的M次方符号的值是唯一的,而当1≤M′<M时,调制符号的M′次方符号在复平面上是对称分布的;之后利用此特征推导出MPSK调制阶数、初始相位和衰落系数估计算法。仿真实验表明,信噪比高于12 d B条件下,HOS算法估计性能与目前平坦慢衰落信道盲估计的主流方法 Lloyd-Max算法相同,而算法复杂度为Lloyd-Max算法的1/50,并且在接收样本符号较少的条件下HOS算法的均方误差曲线收敛于最小二乘估计理论下界。

基于HOS的雷达目标分类新算法

提出一种基于高阶谱 ( HOS)的雷达目标分类方法 ,将双谱概念从频域推广到时域和距离域 ,并给出了将双谱和双相干函数的“均值”及“重心”作为特征量的特征提取新算法 ,在目标散射信号中含有加性噪声和指数噪声的情况下 ,进行了模拟 ,同时对 BSB( Brain State in Box)模型的人工神经网络进行了学习训练和分类仿真 。

HOS运动目标分割算法在视频监控中的应用

为了提高视频监控中运动目标分割的速度和准确度,研究并实现了一种基于高阶统计量HOS(HigherOrderStatistics)的分割算法。首先根据HOS假设检验处理帧差图,判定像素点是否属于运动区域,阈值通过灰度共生矩阵获得,考虑了背景纹理的慢变化。然后,用矩形框聚类法大致确定运动目标的范围,在该范围内使用形态运算法和首尾扫描法去除空洞。最后,使用模板相与法获得帧图像的运动目标模板,从原图像中分割运动区域。算法采用了由粗到精的分析策略,实验表明,是一种快速稳健的算法。

基于HOS的未知雷达辐射源信号分选

对未知雷达辐射源信号进行准确分选是电子对抗领域迫需解决的一个难题。当前用于分选的特征参数主要有相像系数、小波包变换、熵值等，它们普遍存在的问题是在低信噪比下难以获得令人满意的准确率。基于高阶统计量（HOS）可以实现多种不同雷达辐射源信号的分类，且具有抑制高斯噪声的优点。文章提出一种基于HOS的未知雷达辐射源信号分选新方法，通过计算机仿真验证，在相同的信噪比条件下，利用HOS进行分选可以获得更高的准确率。

Modulation recognition of MIMO radar signal based on joint HOS and SNR algorithm

This paper presents a joint high order statistics （HOS） and signal-to-noise ratio （SNR） algorithm for the recognition of multiple-input multiple-output （MIMO） radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.

BLIND ADAPTIVE XPIC BASED ON HOS

This paper presents a new blind XPIC and a new adaptive blind deconvolutional algorithm based on HOS processing, which separates and equalizes the signals in real time. The simulation results demonstrate that the performance of the proposed adaptive blind algorithm,compared with the conventional algorithms, is outstanding with the feature of feasibility, stability and fast convergence rate.

A HOS-based Blind Signal Extraction Method for Chaotic MIMO Systems

A novel method to extract multiple input and multiple output (MIMO) chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal generators was presented. In order to separate the chaotic source signals only by using the sensor signals at receivers, a blind neural extraction algorithm based on higher-order statistic (HOS) technique was used to recover the primary chaotic signals. Simulation results show that the proposed approach has good performance in separating the primary chaotic signals even under nonideal channel.

Analysis on statistic characteristics of magnetotelluric signal

For magnetotelluric sounding (MT), many processing methods based on power spectrum have put forward lots of hypotheses, such as MT signals are Gaussian, linear and minimum-phase. If practical signals do not satisfy these requirements, the results will have a few problems as follows. Firstly, when signals are non-linear and non-Gaussian, the information of the earth contained in the MT signals cannot be sufficiently extracted; Secondly, when signals are non-Gaussian and non-minimum phase, the processed results cannot reflect the minimum phase characteristics of the signals. Hence, it is necessary for us to do further research on characteristics of MT signals (YAO, SUN, 1999; LI, CHENG, 2002; Nikias, Petropulu, 1993; ZHANG, 1996). Otherwise, we cannot judge the reliability of the processed results based on power spectrum.……

Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics

The high penetration of distributed generation(DG)has set up a challenge for energy management and consequently for the monitoring and assessment of power quality(PQ).Besides,there are new types of disturbances owing to the uncontrolled connections of non-linear loads.The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems.Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions.In this context,we propose a measurement method that postulates the use of two-dimensional(2D)diagrams based on higher-order statistics(HOSs)and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign.Being suitable for both PQ and reliability applications,the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform,extracting the individual customers’pattern fingerprint,and compressing the data from both time and spatial aspects.The method allows a continuous and robust performance needed in the SG framework.Consequently,the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.

Detection of Nonlinear Behavior Induced by Hard Limiting in Voltage Source Converters in Wind Farms Based on Higher-order Spectral Analysis

In recent years, sub-synchronous oscillation accidents caused by wind power integration have received extensive attention. The recorded constant-amplitude waveforms can be induced by either linear or nonlinear oscillation mechanisms. Hence, the nonlinear behavior needs to be distinguished prior to choosing the analysis method. Since the 1960s, the higher-order statistics(HOS) theory has become a powerful tool for the detection of nonlinear behavior(DNB) in production quality control wherein it has mainly been applied to mechanical condition monitoring and fault diagnosis. This study focuses on the hard limiters of the voltage source converter(VSC) control systems in the wind farms and attempts to detect the nonlinear behavior caused by bi-or uni-lateral saturation hard limiting using the HOS analysis. First, the conventional describing function is extended to obtain the detailed frequency domain information on the bi-and uni-lateral saturation hard limiting. Furthermore, the bi-and tri-spectra are introduced as the HOS, which are extended into bi-and tri-coherence spectra to eliminate the effects of the linear parts on the harmonic characteristics of hard limiting in the VSC control system, respectively. The effectiveness of the HOS in the DNB and the classification of the hard-limiting types is proven, and its detailed derivation and estimation procedure is presented. Finally, the quadratic and cubic phase coupling in the signals is illustrated, and the performance of the proposed method is evaluated and discussed.

Uncertainties of critical temperatures based on higher-order fluctuations of the largest fragment charge

The new signature of liquid-gas phase transition has been well indicated by the higher-order fluctuations of the largest fragment charge,but the uncertainties of critical temperatures based on this signature have not been revealed.This study extracts the critical temperatures of liquid-gas phase transition in nuclear reactions and investigates their uncertainties.Utilizing the isospin-dependent quantum molecular dynamics model in conjunction with the statistical model GEMINI enables us to describe the dynamical path from the initial to the final state.An isotope thermometer and a quantum fluctuation thermometer are employed to extract the nuclear temperature.The higher-order fluctuations of the largest fragment charge and critical temperatures are studied in^(124)Sn+^(120)Sn collisions ranging from 400 to 1000 MeV/nucleon and^(124)Sn+AZ collisions at 600 MeV/nucleon.Observations revealed that the pseudo-critical point is robustly indicated by the higher-order fluctuations of the largest fragment charge.The critical temperatures extracted by the isotope thermometer are relatively consistent,with an uncertainty of 15%,while those obtained by the quantum fluctuation thermometer are heavily influenced by the incident energy and mass number of target nuclei.The excitation energy E∗and bound charge Zbound are used for event-sorting.These two ensembles represent the statistical properties of the initial and final states of the system,respectively.The initial-final correlations of statistical properties might lead to two phenomena.First,the size distribution of the largest fragment at the pseudo-critical point based on the Zbound ensemble is wide,while that based on E∗ensemble exhibits bimodality,which is a typical characteristic in the liquid-gas coexistence of a finite system.Second,the temperature at the pseudo-critical point based on the Zbound ensemble is higher than that based on the E∗ensemble.Furthermore,the projectile-like system exhibits a significant dynamical effect in its evolution path from the initial to final state,closely associated with the fluctuation of critical temperature.

基于独立分量分析的多次波自适应相减技术

针对多次波自适应相减这个关键问题 ,文中首次提出利用独立分量分析技术来实现多次波和一次波的分离 (简称ICAAMS) .现有的多次波自适应相减技术大都是采用输出信号 (一次波 )能量最小准则 ,基于二阶统计量的技术 .本文提出的ICAAMS采用了输出信号非高斯性最大准则 ,并利用高阶统计量来表征非高斯性 .简单的褶积模型和复杂的有限差分模型资料处理结果表明 ,本文提出的方法可以有效地分离一次波和多次波 .

多特征融合的低景深图像前景提取算法

针对低景深(Low depth-of-field,DOF)图像,提出了一种融合纹理、颜色和高阶统计量(Higher-order statistics,HOS)特征的聚焦前景提取方法.首先,根据相似性最大化原则,通过迭代获得纹理和颜色特征的优化权重,实现低景深图像的区域分割.然后,根据优化权重值计算颜色空间上的加权HOS值,并结合区域归属前景的划分策略,实现低景深图像的前景提取.实验结果表明,该算法可以同时取得较高的主观和客观评价效果.

基于高阶统计量的压缩宽带频谱盲检测方法

针对认知无线网络中宽带频谱感知受到高速模数转换器(ADC)器件的技术限制,利用压缩感知理论(CS),采用压缩信号处理技术,直接对压缩观测数据进行分析,推导出宽带频谱检测的高阶判决统计量的概率分布特性,并在此基础上提出了一种基于高阶统计量的压缩宽带频谱盲检测算法(HOS-CWSBD)。该算法无需任何有关主用户(PU)信号的先验知识、也无需事先重构出原信号就能实现宽带频谱检测。理论分析和仿真结果均表明,与传统的基于压缩感知理论且需要信号重构的压缩频谱感知算法以及基于Nyquist采样数据的非压缩宽带频谱感知算法相比,该算法具有计算复杂度低、感知性能稳定等优点。

基于运动信息与多尺度分水岭的视频目标分割

从视频序列中提取视频目标是基于内容编码中的一项关键技术。提出了将高阶统计运动检测和多尺度分水岭相结合的视频目标分割算法。该算法首先利用高阶统计运动检测算法检测出运动区域,通过后处理得到运动目标的初始模板。然后,用小波变换对视频图像进行多分辨率分解。在最低分辨率上应用分水岭算法分割得到具有精确边缘的分割区域,通过将区域融合后的区域逐步投影到高分辨率图像上并结合高分辨率图像上的分水岭算法逐步提取出具有精确边缘的区域。最后,将运动目标的初始模板和多尺度分水岭分割得到的区域结合起来提取出具有精确边缘的视频对象。实验结果表明该算法能有效地分割和提取出视频序列中的视频对象。

基于经验模态分解/高阶统计法实现微机械陀螺降噪

针对微机电系统(MEMS)陀螺存在的非线性、非平稳噪声,提出了应用经验模态分解/高阶统计(EMD-HOS)的降噪方法对MEMS陀螺进行降噪。首先,采集MEMS陀螺输出信号,根据EMD算法将信号分解成本征模态函数(IMF)。采用Bootstrap技术分别估计各IMF的峰度值,进行高斯特性检验,滤除高斯IMF。接着,使用方差聚合法分别计算IMF的Hurst指数,根据Hurst指数计算阈值,对各IMF进行软阈值处理。将阈值处理后的剩余IMF进行重构,达到降噪的目的。最后,通过交叠式Allan方差分析对滤波前后数据进行处理,绘制Allan方差与相关时间关系曲线,利用非线性最小二乘拟合方法,计算陀螺噪声各项指标。实验表明,EMD-HOS和软阈值处理能够有效地对MEMS陀螺降噪,其信噪比提高了5.6 d B,各项陀螺随机噪声关键指标提高近一个量级。

基于改进RBFN的信号调制识别方法

应用高阶统计量和RBF神经网络原理,针对数字调制通信信号提出了一种基于统计模式识别理论的信号调制类型识别新方法。采用信号四阶和六阶统计量提取信号特征,使用新设计的误差函数训练RBF神经网络,使得识别的效率和正确度得到了明显的改善。计算机仿真结果证明了此方法的可行性。