用增量的累积量方法测量大气风场

为了在非平稳、非线性条件下能得到真实风场的良好估计,提出了一种用空间分布天线（SA）雷达信号的增量累积量测量大气风场参数的方法。该方法基于结构函数方法,利用高阶累计量在信息处理中的特性,用零延迟的增量累计量求解平均水平风速等具体参量，并在大气散射模型基础上对增量累积量方法进行了数值模拟,得出了同理论分析一致的结果.分析和计算表明,该方法完全适用于局地平稳条件，可以减小风场参量估计对天线间距的敏感度；阶数k≥3时,对高斯噪声有很强抑制;同时可以得到风场参量的高阶估计，这是普通的二阶方法（如全相关分析、全谱分析）不具备的。     

A family of lower- and higher-order transversal linearization techniques in non-linear stochastic engineering dynamics

Sample pathwise numerical integration of noise-driven engineering dynamical systems cannot generally be performed beyond a limited level of accuracy, especially when the noise processes are modelled using (filtered) white noises. Recently, a locally transversal linearization (LTL) strategy has been proposed by the author (Proc Roy Soc London A 2001; 457 :539–566) for direct integration of deterministic and stochastic non-linear dynamical systems. The present effort is focussed on a host of extensions along with detailed theoretical error analyses of the linearization approach, especially as applied to problems in non-linear stochastic engineering dynamics. Thus, to begin with, estimates of local and global error orders in the basic LTL scheme are obtained separately for the displacement and velocity vectors when the system is driven either by a set of additive noises or by an arbitrary combination of (independently evolving) additive and multiplicative noises. Following this, a new family of higher-order LTL schemes is proposed in order to improve upon the basic LTL method and the associated error orders are established. A stepwise implementation of the lower- and higher-order versions of the LTL method, along with certain computational aspects, is also outlined. The proposed schemes are numerically illustrated, to a limited extent, for a single degree-of-freedom (SDOF) and a two degree-of-freedom (TDOF) non-linear engineering systems under additive and/or multiplicative white noise excitations. Copyright © 2004 John Wiley & Sons, Ltd.     

地震信号反演的一种新方法

地震反演中,传统反卷积方法对混合相位地震记录不能得到正确的反演结果,本文在格形反卷积的基础上运用全通系统辨识技术,很好地解决了这一问题。考虑到实际地震反演中的非理想因素,本文还引入自适应处理技术用以消除子波时变因素的影响,引入检测技术用以消除噪声的影响。理论和实验均表明本文提出的综合反演方法具有良好的性能。     

一种基于高阶统计信息的信噪比估计改进算法

针对多进制正交幅度调制(Multiple quadrature amplitude modulation,MQAM)信号在低信噪比条件下估计精度不高的问题,提出了一种基于综合利用高阶统计信息的信噪比估计改进算法。根据所选高阶统计量最高阶数的不同,建立了3种信噪比与多种高阶统计量运算式之间的线性关系,利用全回归线性分析方法将3种线性关系转化为3种全回归模型,并求解模型系数。该算法充分利用了多种高阶统计量的有用信息,提高了信噪比估计精度。MQAM的仿真结果表明:在低信噪比条件下,该算法减小了信噪比估计误差,其估计性能明显优于传统的其他算法,且3种模型估计性能依次增加,可依据不同的信噪比要求对3种模型进行选取。     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.     

FREQUENCY DOMAIN TESTS OF MULTIVARIATE GAUSSIANITY AND LINEARITY

A stationary multivariate time series { X _t } is defined as linear if it can be written in the form X _t = ∑^∞ _{j =−∞} A _j e _{t − j} where A _j are square matrices and e _t are independent and identically distributed random vectors. If the e _t } are normally distributed, then { X _t is a multivariate Gaussian linear process. This paper is concerned with the testing of departures of a vector stationary process from multivariate Gaussianity and linearity using the bispectral approach. First the definition and properties of cumulants of random matrices are used to obtain the expressions for the higher-order cumulant and spectral vectors of a linear vector process as defined above. Then it is shown that linearity of a vector process implies constancy of the modulus square of its normalized higher-order spectra whereas the component of such a vector process does not necessarily have a linear representation. Finally, statistics for the testing of multivariate Gaussianity and linearity are proposed.     

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 short-wave 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 cumulants 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.     

基于四阶累积量的波束域MUSIC方法

在非白噪声背景下,基于二阶统计量的高分辨方法性能较差.基于四阶累积量的高分辨方法能较好地抑制空间高斯噪声,但其运算量较大.为了解决这一矛盾,文章提出了一种基于四阶累积量的波束域MUSIC方法.仿真分析和实验结果表明,与阵元域四阶累积量MUSIC方法相比,论文所提方法降低了分辨门限,减小了估计偏差和均方根误差,同时减小了运算量.     

基于四阶累积量的时延估计新方法

针对无线电定位中的时延估计问题,考虑接收信号为窄带信号以及背景噪声为高斯分布的情况下,将四阶累积量的一维切片作为高阶统计量,结合希尔伯特变换,给出一种新的时延估计方法.该方法对相关峰值进行了锐化处理,提高了估计精度.与广义互相关法相比,该算法可有效抑制空间相关高斯噪声的影响,计算量小,易于实现.仿真结果表明了该方法的有效性.     

高阶逻辑下知识表示与聚类方法的研究*

针对一阶逻辑在复杂结构数据环境中存在模式搜索空间庞大和不能发明新谓词的缺点,提出了使用类型化的高阶逻辑知识表示语言Escher去表示各种复杂结构的数据,利用其强类型语法有效地约束知识发现过程中模式的搜索空间和高阶的特点去解决新谓词构造的问题。设计了以Escher为基础的复杂结构数据中的知识发现过程和基于复杂结构数据的聚类算法,并以实验验证了其有效性。