一种基于平坦测度的瞬态信号检测技术

在感知音频编码中,需要对出现瞬态信号的帧进行特殊处理,其前提是合理地检测出瞬态信号。基于瞬态信号的时域和频域特点,提出一种利用信号的平坦测度作为判决函数的时-频瞬态检测方法。通过MATLAB软件仿真测试,此方法在多数测试音乐中的漏检和误检数量较少,且算法复杂度较低。与已有的瞬态检测方法相比,此方法在漏检和误检方面有所改善,且算法复杂度随着瞬态信号的明显程度自适应的变化,具有检测准确度高、算法简单等优点。     

非高斯水声瞬态信号双通道Power-Law检测

根据水声瞬态信号的特点,在分析能量检测和高阶谱检测的特性基础上,研究了基于能量检测和基于高阶谱的Power-Law检测方法,将上述两种检测器联合构成一种双通道Power-Law检测器,更好地利用了水声瞬态信号中的统计信息。通过计算机仿真试验,证明了该检测器的有效性,它能在更大程度上适应复杂水声环境变化的要求,改善和提高水声瞬态信号的检测性能。     

基于滤波器组和高阶累积量的信号特征检测

结合高阶累积具有对加性高斯和对称非高斯噪声不敏感的特性，提出了一种基于滤波器组和高阶累积量的特征检测方法。该方法既不需待检信号做高斯和平稳的假设，也不需要有信号的先验知识。其原理是首先通过滤波器组将检测信号在频域上进行分离，选取输出能量较大的一组子频带信号近似给出信号的时频描述；然后在各个选中的子频带内分别计算三阶累积量的短时估计，从而抑制有色噪声，将信号的特征检测出来。仿真信号和实验信号验证了该方法的有效性和适应性，即使信号特征完全淹没在噪声中，也能检测出来。     

水下混沌背景中的瞬态声信号检测法研究

水下瞬态声信号中蕴含着目标的特征信息,但其突发性强、持续时间短致使检测难度很大。为解决瞬态信号检测的问题,提出了混沌背景中瞬态冲击信号的RBF神经网络检测法。建立了混沌背景噪声的一步预测模型,通过预测误差的变化来检测瞬态信号。分别以Lorenz系统和Logistic系统作为混沌背景噪声进行了仿真,证明检测方法的有效性,并在Lorenz系统背景检测中加入白噪声来检验该方法抗白噪声干扰的能力,结果表明该方法对白噪声敏感;在理论研究的基础上通过对外场试验数据的处理验证了该方法的有效性,并在实际测量数据中加入混沌背景噪声,通过改变信噪比检验了该方法在不同信噪比情况下的性能。     

主动声纳中累积和检验算法门限确定方法及应用

研究了主动声纳抗多途信号处理方法——累积和检验算法的门限确定方法。采用矩阵法计算其虚警性能,推导了门限计算公式,在非时变的假设下,论证了计算门限所设定的最低可检测信噪比10dB的合理性,分析了当回波序列中目标处实际信噪比与设定的最低可检测信噪比不符合时,该算法虚警性能的变化。仿真和海试数据验证了这种门限确定方法的有效性。     

带趋势项GARCH误差模型参数变化的残量检验

本文研究带趋势项GARCH误差模型的参数变点检验.在参数未知的情况下,利用残量累积和检验以消除相依序列对检验结果的影响:研究表明在原假设条件成立下,残量累积和统计量的极限分布是一个标准布朗桥的上确界.蒙特卡罗数值模拟和IBM收益率数据分析的结果都充分说明了本文方法的有效性和实用性.     

基于小波变换的主动水声信号检测

信号检测是水声信号处理领域的研究重点之一。文章通过对水下航行器自噪声特性的研究，发现其具有1／f衰减特性，同时利用小波变换对1/f信号有类似K—L展开的作用，提出了一种基于小波变换的信号检测方法，对1/f噪声背景下的主动水声信号进行了检测；文中详细推导了相应的检验统计量及其统计分布特性；同时，将该方法与匹配滤波器、能量检测器的检测性能进行了比较，仿真试验结果表明了本方法的有效性。     

基于连续小波和统计检验的瞬态成分检测与应用

将连续小波变换和统计检验结合用于检测信号中具有一定时频分布的瞬态成分，提出了一种基于连续小波变换和统计检验的瞬态成分检测方法，并用于圆锥滚子轴承振动中的瞬态成分的检测与提取，比较有效地检测出瞬态成分，并在此基础上应用连续小波变换反演得到瞬态成分的估计，表示轴承的状态特征，作为轴承故障诊断的依据。     

飞机结构腐蚀损伤分布规律研究

选取四种分布对现役飞机结构腐蚀损伤进行统计特征研究。结果表明最大腐蚀深度服从三参数Weibull分布 ,该分布较好地反映了腐蚀损伤累积规律。统计检验采用Pearson统计量—线性相关系数r方法进行。满足假设分布的临界r值采用r -t分布函数变换得到。采用相关系数优化法求得三参数Weibull分布的位置参数。     

几种水声瞬态信号检测算法的比较研究

对于空投浮标、鱼雷等反潜武器的入水瞬态信号进行检测一直是水声对抗领域的一项关键技术。本文针对入水瞬态信号介绍了4种常用的统计量检测方法,并通过蒙特卡洛仿真和湖试数据处理比较了4种检测方法的检测效果,分析了各自的特点,对水声瞬态信号检测具体工程应用具有较好的参考价值。     

Simulation of stationary Gaussian/non-Gaussian stochastic processes based on stochastic harmonic functions

A new model is proposed to represent and simulate Gaussian/non-Gaussian stochastic processes. In the proposed model, stochastic harmonic function (SHF) is extended to represent multivariate Gaussian process firstly. Compared with the conventional spectral representation method (SRM), the SHF based model requires much fewer variables and Cholesky decompositions. Then, SHF based model is further extended to univariate/multivariate non-Gaussian stochastic process simulation. The target non-Gaussian process can be obtained from the corresponding underlying Gaussian processes by memoryless nonlinear transformation. For arbitrarily given marginal probability distribution function (PDF), the covariance function of the underlying multivariate Gaussian process can be determined easily by introducing the Mehler’s formula. And when the incompatibility between the target non-Gaussian power spectral density (PSD) or PSD matrix and marginal PDF exists, the calibration of the target non-Gaussian spectrum will be required. Hence, the proposed model can be regarded as SRM to efficiently generate Gaussian/non-Gaussian processes. Finally, several numerical examples are addressed to show the effectiveness of the proposed method.     

高斯流场中单自由度线性体系非高斯振动响应试验研究

对于风湍流等高斯分布流速场中的线性结构体系,当考虑荷载中脉动流速二次项的影响时,理论上其振动响应将呈现非高斯分布特性。基于调试得到的不同粗糙工况高斯流场,开展了单自由度线性体系顺流向振动响应测试,研究了单自由度线性体系加速度响应的非高斯分布特性,分析了粗糙度对响应非高斯成分的影响,讨论了三种常见非高斯概率密度逼近方法对响应的拟合效果。试验结果表明:试验高斯流场中单自由度线性体系的顺流向加速度响应主要呈现出尖峰非高斯分布特征,且随着紊流度的提高,响应非高斯性有增强的趋势;响应的非高斯概率密度宜采用高斯混合模型方法进行拟合。     

A copula-based Gaussian mixture closure method for stochastic response of nonlinear dynamic systems

Gaussian closure method is commonly used in the analysis of nonlinear stochastic systems. However, Gaussian closure may lead to unacceptable errors when system response is very much different from being Gaussian, and accuracy of the method decreases as the nonlinearity of the system increases. The need for better accuracy in strongly non-linear problems has caused the development of non-Gaussian closure schemes. In this paper, we develop a new copula-based Gaussian mixture closure method for randomly excited nonlinear systems. Our method relies on the assumption of marginal PDF of response in terms of finite Gaussian mixture model, and the derivation of joint PDF with aid of dependence modeling of Gaussian copula. By substituting the non-Gaussian PDF representation into moment equations of nonlinear system, we further develop an optimization-based closure scheme for the solution of the unknown parameters in joint PDF. In this way, PDF and thus, moments of response of highly nonlinear system can be described in a more flexible and robust way. Effectiveness of the new closure method is demonstrated by a nonlinear and a Duffing oscillator that are subjected to Gaussian white noise. The results are compared with the Gaussian closure and exact solution. It has been shown that Gaussian closure is a special case of the new closure method, and accuracy of Gaussian closure is the lower bound of that of the new closure method.     

Evaluation of bistable systems for binary signal detection in symmetric non-Gaussian noise

For detecting binary signals in symmetric noise with unknown probability density functions (PDF), a nonlinear receiver is proposed based on the bistable systems with autoregressive models of order 1 [AR(1)]. The bistable systems are utilized to pre-process the noisy observations ahead of the linear correlation (LC) detector. The permutations of the observations are utilized to bypass the design of the optimal LC vector which depends on the noise PDF. The detection performances, in the form of probabilities of error, in some non-Gaussian noise are evaluated versus the matched filter (MF) and Volterra filter (VF) through numerical simulations. The results show that the bistable receiver performs better than MF receiver when the noise deviates from Gaussian distribution, and seems more robust compared to the VF receiver.     

MIMO线性系统输入输出信号统计特征的双谱评定方法

基于高阶统计量具备处理随机信号的特性,提出了一种利用三阶谱(双谱)评定MIMO线性系统时域输入输出信号统计特征的新方法。通过建立线性系统双谱数学模型,根据系统响应、所测得的频响函数以及离散信号的双谱数值估计算法,经逆运算获得系统的双谱驱动信号,随后利用高阶谱对高斯随机信号的盲性判定其输入信号的高斯性。将上述方法与采用传统相位随机化法(对功率谱添加随机相位)所获得的驱动信号分别应用于一悬臂梁模拟控制系统中,通过对输入信号的分析及控制结果的比较,发现基于双谱所生成的时域随机驱动信号呈现出较强的非高斯性且收敛速度更快。对于输出信号统计特征的评定,提出从输入信号与系统频带接近的程度入手,再次利用高阶统计量对高斯随机信号的盲性进行定性判定,对于无法判别满足何种非高斯统计分布特征的,不管是对于输入信号还是输出信号,一律采用绘制信号的概率分布特征曲线进行定量评定。     

混响背景下GTM回波检测算法研究

为改善混响背景下传统匹配滤波算法效果不佳问题,在分析其非平稳性、有色性和非高斯性的基础上,提出了混合高斯时变自回归模型(Gaussian mixture Tvar Model,GTM),推导了模型公式及其参数求解方法,形成了GTM回波检测算法。为对混响特性及滤波效果进行定量描述进而验证算法性能,给出了一种定量衡量混响非平稳性、有色性、非高斯特性的滤波效果评价方法。通过实测混响分析表明,GTM模型能够较好地拟合实测混响的概率密度曲线和功率谱密度曲线,实现了混响背景下回波的有效检测并改善混响特性。     

小波变换域双谱分析及其在滚动轴承故障诊断中的应用

工程信号不仅会受到高斯噪声干扰，而且也会受到非高斯噪声干扰。而传统双谱分析方法从理论上仅能抑制高斯噪声，但对非高斯噪声是无能为力的。针对传统双谱存在的不足，将小波变换和双谱分析结合，提出了一种基于小波变换域非参数化双谱故障诊断方法，并应用到滚动轴承故障诊断中。考虑到滚动轴承信号幅值调制特点，在本方法中，对处理信号采用了希尔伯特变换技术，以进行解调。实验结果表明，小波域双谱优于传统双谱，特别是在非高斯噪声情况下，小波域双谱更有优势；研究为滚动轴承故障诊断提供了一种新的有效方法。     

A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic process by a translation process

Some widely used methodologies for simulation of non-Gaussian processes rely on translation process theory which imposes certain compatibility conditions between the non-Gaussian power spectral density function (PSDF) and the non-Gaussian probability density function (PDF) of the process. In many practical applications, the non-Gaussian PSDF and PDF are assigned arbitrarily; therefore, in general they can be incompatible. Several techniques to approximate such incompatible non-Gaussian PSDF/PDF pairs with a compatible pair have been proposed that involve either some iterative scheme on simulated sample functions or some general optimization approach. Although some of these techniques produce satisfactory results, they can be time consuming because of their nature. In this paper, a new iterative methodology is developed that estimates a non-Gaussian PSDF that: (a) is compatible with the prescribed non-Gaussian PDF, and (b) closely approximates the prescribed incompatible non-Gaussian PSDF. The corresponding underlying Gaussian PSDF is also determined. The basic idea is to iteratively upgrade the underlying Gaussian PSDF using the directly computed (through translation process theory) non-Gaussian PSDF at each iteration, rather than through expensive ensemble averaging of PSDFs computed from generated non-Gaussian sample functions. The proposed iterative scheme possesses two major advantages: it is conceptually very simple and it converges extremely fast with minimal computational effort. Once the underlying Gaussian PSDF is determined, generation of non-Gaussian sample functions is straightforward without any need for iterations. Numerical examples are provided demonstrating the capabilities of the methodology.     

基于高斯混合模型的非高斯随机振动幅值概率密度函数

针对非高斯振动信号的幅值概率密度函数难以用数学模型表述的问题,提出了基于高斯混合模型的非高斯概率密度函数表示方法。首先,基于时域样本信号得到非高斯振动信号的高阶矩估计值。其次,基于高斯随机过程偶次高阶矩之间的定量关系,结合二阶高斯混合模型建立方程组,求解得到混合模型中每个高斯分量的方差和权值。然后,将各高斯分量的权值和方差代入高斯混合模型,得到适用于对称非高斯振动信号的幅值概率密度函数。最后,通过仿真信号和实测振动信号,验证了该方法的有效性和适用性。     

Simulation of a non-Gaussian stochastic process based on a combined distribution of the UHPM and the GBD

To simulate non-Gaussian stochastic processes based on the first four moments, various simulation methods are presented, in which the determination of the transformation model and the calculation of the correlation coefficients between non-Gaussian stochastic processes and Gaussian stochastic processes are the critical procedures in these simulation methods. However, some existing simulation methods are limited to specific ranges. Furthermore, their practical applications are affected negatively due to the expensive cost of determining the transformation model and the correlation coefficients between non-Gaussian and Gaussian stochastic processes. Therefore, an accurate and efficient simulation method of a non-Gaussian stochastic process with a broader range is proposed in this article. Since the simulation of non-Gaussian processes and the Nataf transformation of non-Gaussian variables have some similar characteristics, a new combined distribution is proposed based on the unified Hermite polynomial model (UHPM) and the generalized beta distribution (GBD). Then, the combined distribution is employed in the simulation of non-Gaussian stochastic processes, in which the transformation model is deduced by the combined distribution. The correlation coefficient transformation function (CCTF) between the Gaussian and non-Gaussian stochastic processes can be evaluated through the interpolation method. Furthermore, numerical examples are presented to show the accuracy and effectiveness of the proposed simulation method for non-Gaussian stochastic processes.