基于回归移位寄存器的频率转移函数

介绍了差分跳频技术的特点,构造了一类基于回归移位寄存器的频率转移函数.此类频率转移函数具有最大化的自由距,并且具有实现简单,可控参数多的特点.应用马尔科夫链理论对构造的频率转移函数的性能进行了分析.证明了基于回归移位寄存器的频率转移函数产生的频率序列是遍历的马尔科夫链,给出了该链到达平稳状态所需的状态转移步数.     

一种基于马氏链的可容侵网络状态评估模型

分析了可容侵网络的运行状态转移过程,在此基础上提出了一种基于马氏链的可客侵网络状态评估模型,并给出了计算实例.该模型可用于对可容侵网络性能进行评估.     

相关跳频序列的Viterbi译码算法及其纠错性能分析

该文分析了相关跳频通信系统中频率转移函数的纠错性能,将Viterbi算法应用于接收端跳变频点序列的译码,并结合相关跳频转移函数的特点对译码过程作出了改进.针对满足最大相关纠错性能的频率转移函数,详细分析了Viterbi算法的译码性能.仿真结果表明,通过相关纠错,对随机错误在低信噪比条件下可以获得5-8dB的增益.     

T函数Walsh谱值与差分转移概率快速算法

根据T函数自身输入与输出结构特点,结合T函数窄度相关定义,研究了T函数线性性质和差分性质。通过构造马尔可夫链和概率转移矩阵,给出了其Walsh谱值与差分转移概率计算的多项式时间快速算法,时间复杂度为O(n),并将该算法应用于对TSC系列T函数的研究,得到任意输入输出线性组合的Walsh谱值表达式。     

由《现代控制理论》教材习题引发的讨论

许多文献对状态转移矩阵eAt的性质和计算方法进行了广泛的讨论.但给定一个矩阵函数,如何判断它是否是某一连续时间线性时不变系统的状态转移矩阵,在相关的教材中却没有涉及.本文根据常微分方程解的唯一性得到了判断一个矩阵函数是某一连续时间线性时不变系统的状态转移矩阵的充分必要条件,并求出了其对应的系统矩阵.此外,若状态转移矩阵Φ(t)已知,本文也给出了求解对应的系统矩阵的三种方法.     

马氏调节反射O-U过程的平稳分布

讨论了一类特殊随机过程,即马氏调节双边反射带跳的O-U过程。其中的参数和跳均被一个有限状态空间、连续时间的齐次不可约马氏链所调节。文中的主要目的是证明该过程的平稳分布满足一个交互积分-微分方程。尤其当,马氏链只有两个状态时,讨论交互积分-微分方程解的存在唯一性问题,并在给出合适的边界条件下进行数值说明。     

基于马尔科夫链的灰狼优化算法收敛性研究

针对灰狼优化算法（Grey Wolf Optimization,GWO）在收敛性研究上的不足,首先,通过定义灰狼群状态转移序列,建立了GWO算法的马尔科夫（Markov）链模型,通过分析Markov链的性质,证明它是有限齐次 Markov链;其次,通过分析灰狼群状态序列最终转移状态,结合随机搜索算法的收敛准则,验证了GWO算法的全局收敛性;最后,对典型测试函数、偏移函数及旋转函数进行仿真实验,并与多种群体智能算法进行对比分析.实验结果表明,GWO算法具有全局收敛性强、计算耗时短和寻优精度高等优势.     

基于受控马氏链的干涉型光纤陀螺温度漂移模型

导出了单输入单输出(SISO)系统马氏链模型和差分方程的关系;给出了状态转移概率矩阵估计的具体方法;最后,依据某型干涉型光纤陀螺(IFOG)实际测试数据,进行了仿真研究。结果表明,采用本文的方法可以对光纤陀螺温度漂移进行辨识,和同类方法比较,具有较好的预测效果。     

改进的K步长多模式匹配算法

K步长状态机存在失效函数,一部分存储空间被用来存储失效状态。为了提高K步长状态机的空间性能,该文通过K步长状态机的转移函数和失效函数f构建了新的转移函数,消除了K步长状态机的"失效链"。对改进算法进行性能分析表明,模式串数量和长度越大,改进后算法的空间优化效果越明显。     

基于准确状态模型的单星对星测角和测频无源定轨方法

在星对星无源定轨跟踪中,由于卫星运动方程的非线性和隐函数特性,如何建立适合递推滤波的状态预测方程和状态转移矩阵是个首要的问题.根据目标星与观测星相对速度很大的特点,在角度观测之外引入频率观测,然后根据卫星运动的动力学和几何学条件,推导准确的状态预测方程及其对应的状态转移矩阵,并给出了相应的计算方法.结合EKF滤波算法,提出了一种新的基于准确状态预测模型的单星对星测角和测频无源定轨跟踪方法.仿真实例表明,该方法定位性能较仅测角定位法有很大程度改善;且与已有的基于近似状态预测模型的利用角度和频率信息的定轨方法相比,本文方法亦具有更高的估计精度和更快的收敛速度,且两者计箅耗时相当.     

On Probability Density for Modeling Video Traffic

Accurate models for variable bit rate (VBR) video traffic need to allow for different frame types present in the video, different activity levels for different frames, and a variable group of pictures (GOP) structure. The temporal as well as the stochastic properties of the trace data need to be captured by any models. We propose some models that capture temporal properties of the data using doubly Markov processes and autoregressive models. We highlight the importance of capturing the stochastic properties of the data accurately, as this leads to significant improvement in the performance of the model. In order to capture the stochastic properties of the traces, the probability density function of the trace data needs to be accurately modeled. Hence, the focus of this paper is on creating autoregressive processes with arbitrary probability densities. We relate this to work in wavelet theory on the solutions to two-scale dilation equations. The performance of our model is evaluated in terms of the stochastic properties of the generated trace as well as using network simulations.     

Direct-Detection Optical Communication Receivers

A model that is sufficiently general to describe the predominant statistical characteristics of the output of many real optical detectors is formulated. This model is used to study the optimum receiver processing for direct-detection optical communication systems. In particular, the structures of detectors and estimators for randomly filtered doubly stochastic Poisson processes observed in additive white Gaussian noise are considered. Representations for the posterior statistics of a vector-valued Markov process that modulates the intensity of the doubly stochastic Poisson process are obtained. Quasi-optimum estimators and detectors are specified in general terms and specialized for several important applications. These include a demodulator for subcarrier angle modulation, a detector structure for binary signaling with known intensities, and a detector structure for binary signaling in the turbulent atmosphere.     

Recursive estimation of parameters in Markov-modulated Poissonprocesses

A hidden Markov regime is a Markov process that governs the time or space dependent distributions of an observed stochastic process. Recursive algorithms can be used to estimate parameters in mixed distributions governed by a Markov regime. The authors derive a recursive algorithm for estimation of parameters in a Markov-modulated Poisson process also called a Cox point process. By this the authors mean a doubly stochastic Poisson process with a time dependent intensity that can take on a finite number of different values. The intensity switches randomly between the possible values according to a Markov process. The authors consider two different ways to observe the Markov-modulated Poisson process: in the first model the observations consist of the observed time intervals between events, and in the second model they use the total number of events in successive intervals of fixed length. They derive an algorithm for recursive estimation of the Poisson intensities and the switch intensities between the two states and illustrate the algorithm in a simulation study. The estimates of the switch intensities are based on the observed conditional switch probabilities     

An EM Algorithm for Markov Modulated Markov Processes

An expectation–maximization (EM) algorithm for estimating the parameter of a Markov modulated Markov process in the maximum likelihood sense is developed. This is a doubly stochastic random process with an underlying continuous-time finite-state homogeneous Markov chain. Conditioned on that chain, the observable process is a continuous-time finite-state nonhomogeneous Markov chain. The generator of the observable process at any given time is determined by the state of the underlying Markov chain at that time. The parameter of the process comprises the set of generators for the underlying and conditional Markov chains. The proposed approach generalizes an earlier approach by RydÉn for estimating the parameter of a Markov modulated Poisson process.      

Iterative algorithms for state estimation of jump Markov linearsystems

Jump Markov linear systems (JMLSs) are linear systems whose parameters evolve with time according to a finite state Markov chain. Given a set of observations, our aim is to estimate the states of the finite state Markov chain and the continuous (in space) states of the linear system. In this paper, we present original deterministic and stochastic iterative algorithms for optimal state estimation of JMLSs. The first stochastic algorithm yields minimum mean square error (MMSE) estimates of the finite state space Markov chain and of the continuous state of the JMLS. A deterministic and a stochastic algorithm are given to obtain the marginal maximum a posteriori (MMAP) sequence estimate of the finite state Markov chain. Finally, a deterministic and a stochastic algorithm are derived to obtain the MMAP sequence estimate of the continuous state of the JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problem of deconvolution of Bernoulli-Gaussian (BG) processes and the problem of tracking a maneuvering target are addressed     

Quality assessment models for initial production process control based on stochastic differential equations

It is of great importance to assess an initial production process prior to the regular mass-production. For this purpose, many statistical methods have been proposed for practical use. In this paper, we propose two stochastic models for an assessment method of the initial production process control: a Markov process model and a Markov approximation model. These models are continuous state space models and formulated by applying mathematical techniques of stochastic differential equations. Based on each model, we derive several quantitative assessment measures for initial production process control.     

On first-order Markov modeling for the Rayleigh fading channel

Previous models for the received signal amplitude of the flat-fading channel that use first-order finite-state Markov chains are examined. The stochastic properties of a proposed first-order model based on these models are examined. The limitations of using an information theoretic metric, which is sometimes used to justify a first-order Markov chain as a sufficient model for very slowly fading channels, are discussed. A simple method of qualitatively comparing autocorrelation functions is instead proposed. The usefulness of the first-order Markov chain in representing the flat-fading channel is examined by looking at two specific problems in wireless system applications that represent two disparate cases. The first case involves analysis over a short duration of time, relative to the inverse of the normalized Doppler frequency, while the second involves analysis over a long duration of time. Contrary to previous reports, the results indicate that first-order Markov chains are not generally suitable for very slowly fading channels. Rather, first-order Markov chains can be suitable for very slowly fading applications, which require analysis over only a short duration of time     

THREE-SPACE-HARMONIC INTERACTION IN DOUBLY SINUSOIDAL PERIODIC MEDIA

Three-space-harmonic (3SH) interaction in doubly sinusoidal periodic (DSP) medium is investigated. Associated physical effects such as additional gap, defect state, and indirect gaps, are theoretically and numerically revealed. This simple DSP model can facilitate the understanding and utilizing of a series of effects in rather complicated periodic structures with additional defect or modulation.     

Asymptotic redundancy of the MTF scheme for stationary ergodic sources

The Move-to-front (MTF) scheme is a data-compression method which converts each symbol of a source sequence to a positive integer sequentially, and encodes it to a binary codeword. The compression performance of this algorithm has been analyzed usually under the assumption of the so-called symbol extension. But, in this paper, upper and lower bounds are derived for the redundancy of the MTF scheme without the symbol extension for stationary ergodic sources and Markov sources. It is also proved that for the stationary ergodic first-order Markov sources, the MTF scheme can attain the entropy rate if and only if the transition matrix of the source is a kind of doubly stochastic matrix. Moreover, if the source is a Kth-order Markov source (K/spl ges/2), the MTF scheme cannot attain the entropy rate of the source generally.     

时域有限差分法分析平面周期性结构电磁特性

本文阐述了如何用时域有限差分法分析平面周期结构的电磁特性.FDTD方法与周期性边界条件、吸收边界条件结合,构成分析各种周期性结构电磁特性准确、有效的通用工具.但为了在时域表示宽带信号的周期性边界条件,必须引入辅助场量并加以适当变形才可建立时域迭代式.文中以平面双周期频率选择表面为例,给出了计算思路和结果.