探测、跟踪与侦察

Y2000-62000-737 0009979目标跟踪(含7篇文章)=Target Tracking[会,英]//1998 37th IEEE Conference on Decision and Control.Vol.1 of 4.—737～765(NiG)本部分收录7篇论文。内容包括采用自适应卡尔曼滤波器的机动目标跟踪,杂波中跟踪机动目标的马尔可夫链蒙特卡洛方法,具有导航不确定性的多目标跟踪,跟踪小目标采用随机采样数据的数据融合,参数和状态估计。     

一款微处理器芯片电源网络的验证及优化

当CMOS工艺进入深亚微米设计阶段,器件密度和时钟频率增加,电源线和地线网络传送的电流也同样增加,导致功率密度的增加,这些都将对电源网络产生不利影响。因此,设计良好的电源网络显得尤为重要。为了确保较短的设计周期以及满足可靠性和可制造性的要求,电源网络的验证及优化已成为整个设计流程中关键的一步。首先介绍了存在于电源网络的欧姆电压效应,并且给出了一款系统芯片的电源网络的验证优化流程,基于此流程进行欧姆压降的分析,在尽量缩小设计周期的考虑下,给出了优化的几个方法,并应用于工程实际,达到了设计要求。     

基于马尔可夫过程的UPS可靠性指标的计算

不间断电源(UPS)是在电网异常的情况下不间断地为电器负载设备提供后备交流电源,以维持电器正常运作的设备,目前其已在诸多领域中得到了广泛的应用.因此,基于六性协同工作平台的马尔可夫过程模块对某UPS系统的可靠性指标进行了认证和分析.首先,简单地介绍了UPS系统及其可靠性建模方法;其次,概述了可维修系统的马尔可夫过程求解;然后,阐述了六性协同工作平台和马尔可夫过程模块;最后,对利用六性协同工作平台的马尔可夫过程模块计算UPS系统可靠性指标的具体过程进行了详细的介绍,对于快速地求解UPS系统的可靠性水平具有重要的意义.     

基于局部模板匹配的运动目标跟踪

针对环境中障碍物对被跟踪目标构成不可预知的遮挡问题,提出了一种新的基于局部区域特征匹配的跟踪算法.首先采用一组基本观察片图模拟目标的外观;其次提出了一种将运动轨迹特性与动态模型结合的采样结构,采用马尔可夫链蒙特卡洛(MCMC,Markov chain Monte Carlo)方法独立估计每个基本片图的状态,并使用运动区...     

含有非马尔可夫过程的排队Petri网模型和性能分析

本文提出了一种新的高级性能模型技术,称作NM-QPN(含有非马尔可夫过程的排队Petri网,Queuing Petri Net including Non-Markovian processes),它综合了排队网,随机Petri网以及模拟求解各自的特点.NM-QPN以模拟模型为总体框架,发挥排队网和随机Petri网各自的优势对系统进行建模.提出了一套完整的NM-QPN模型求解方案,通过流等价方法将模型中的马尔可夫过程进行化简,这样可以大大减少模型的状态,最后再用模拟求解方法求解剩下的非马尔可夫过程.     

一种复杂网络传递特性分析方法

基于工程中常见的复杂信息系统网络模型,提出一种针对网络传递特性的分析方法,通过网络鲁棒性与时延性两个性质来表征网络传递特性.首先,构建网络子图重要度指标以表征鲁棒性,并基于蒙特卡洛方法进行鲁棒性仿真分析;其次,构建时延代价函数以表征时延性,并通过基于约束的蚁群算法寻找最短时延,进行时延性仿真分析;最后,根据分析结果,验...     

基于信赖域的序贯拟蒙特卡洛滤波算法

针对系统状态估计、目标跟踪等是包含多源不确定性信息的非线性非高斯随机过程,提出了一种基于信赖域的序贯拟蒙特卡洛(Sequential Quasi-Monte Carlo,SQMC)滤波算法.该算法利用拟蒙特卡洛积分技术优化采样粒子在状态空间的分布特性,降低了滤波过程中的积分误差,提高了状态估计精度;同时,利用信赖域(T...     

一种动态电源管理预测算法的设计及仿真

针对基于马尔可夫模型的预测式动态电源管理算法（DPMPA）对大型样本数据预测精度低的问题,提出了一种具备自反馈功能的内嵌式马尔可夫模型（RMM）的DPMPA。该算法基于分层迭代思想,对满足马尔可夫性质的大型数据进行聚类,再使用马尔可夫算法对构建出的迭代数据模型：上层抽象数据模型和底层实例数据模型进行训练。引入反馈函数φ（i）,控制转换概率矩阵更新频率,保证预测精度范围。依此,编制了自反馈内嵌式马尔可夫模型DPMPA的Matlab程序。应用该程序对无线热点访问次数进行仿真预测,得出不同训练样本数对后期样本的预测精度的影响,对比马尔可夫算法和自适应学习树（ALT）算法预测结果表明,基于该自反馈RMM预测式动态电源管理算法对于大型样本数据预测精度比前者高5%,后者高10%。预测精确度的提高,将更有利于马尔可夫算法的DPM系统功耗控制。     

Mesh结构电源/地布线网络分析方法综述

在深亚微米VLSI设计中，获得电源／地布线网络优化设计的关键是在设计周期里对其进行有效的分析。文章在叙述Mesh结构电源／地布线网络分析模型的基础上，简要介绍了电源／地布线网络分析方法的研究进展，并指出了该研究领域存在的一些问题。     

基于不完全分解预优共轭梯度法的电源和地线网络求解器

在超大规模集成电路的电源和地线网络的设计中,求解由该网络上每个节点的电压和每条边上的电流是最基本的运算,它对电源和地线网络拓扑结构设计和线宽优化算法的质量具有直接的影响.针对电源和地线网络的特殊性,提出了一个高效的电源和地线网络求解器,包括电路网络中树结构的合并与恢复和用不完全分解的预优共轭梯度法来求解节点电压方程.该求解器的运算速度很快,所耗费的内存很小,同时具有很强的鲁棒性.     

并行MCMC算法的SAR影像分割

MCMC(Markov Chain Monte Carlo, MCMC)方法采用顺序改变表征像素类属性的标号变量值会导致算法运算时间长、收敛速度慢等问题。为此,本文提出并行化改变像素标号值的MCMC方案,在贝叶斯推理框架下,依据高斯分布及MRF(Markov Random Field, MRF)模型建立SAR(Synthetic Aperture Radar, SAR)影像分割模型,设计实现基于多线程的并行采样方案;为了解决MRF标号场中邻域像素标号相关性问题,提出独立的像素并行采样的准则;同时,限制并行线程的数量,以保证采样的随机性。运用传统的串行算法和提出的并行算法对模拟和真实SAR影像进行影像分割实验;定性和定量的时间和精度评价结果表明:该方案在不影响分割精度的前提下大幅缩短影像分割时间,提高了效率。      

Abrupt motion tracking via intensively adaptive Markov-chain Monte Carlo sampling

The robust tracking of abrupt motion is a challenging task in computer vision due to its large motion uncertainty. While various particle filters and conventional Markov-chain Monte Carlo (MCMC) methods have been proposed for visual tracking, these methods often suffer from the well-known local-trap problem or from poor convergence rate. In this paper, we propose a novel sampling-based tracking scheme for the abrupt motion problem in the Bayesian filtering framework. To effectively handle the local-trap problem, we first introduce the stochastic approximation Monte Carlo (SAMC) sampling method into the Bayesian filter tracking framework, in which the filtering distribution is adaptively estimated as the sampling proceeds, and thus, a good approximation to the target distribution is achieved. In addition, we propose a new MCMC sampler with intensive adaptation to further improve the sampling efficiency, which combines a density-grid-based predictive model with the SAMC sampling, to give a proposal adaptation scheme. The proposed method is effective and computationally efficient in addressing the abrupt motion problem. We compare our approach with several alternative tracking algorithms, and extensive experimental results are presented to demonstrate the effectiveness and the efficiency of the proposed method in dealing with various types of abrupt motions.     

Support vector regression via MCMC within evidence framework

This paper proposes a novel approach, Markov Chain Monte Carlo (MCMC) sampling approximation, to deal with intractable high-dimension integral in the evidence framework applied to Support Vector Regression (SVR). Unlike traditional variational or mean field method, the proposed approach follows the idea of MCMC, firstly draws some samples from the posterior distribution on SVR??s weight vector, and then approximates the expected output integrals by finite sums. Experimental results show the proposed approach is feasible and robust to noise. It also shows the performance of proposed approach and Relevance Vector Machine (RVM) is comparable under the noise circumstances. They give better robustness compared to standard SVR.     

Modeling the haemodynamic response in fMRI using smooth FIR filters

Modeling the haemodynamic response in functional magnetic resonance (fMRI) experiments is an important aspect of the analysis of functional neuroimages. This has been done in the past using parametric response function, from a limited family. In this contribution, we adopt a semi-parametric approach based on finite impulse response (FIR) filters. In order to cope with the increase in the number of degrees of freedom, we introduce a Gaussian process prior on the filter parameters. We show how to carry on the analysis by incorporating prior knowledge on the filters, optimizing hyper-parameters using the evidence framework, or sampling using a Markov Chain Monte Carlo (MCMC) approach. We present a comparison of our model with standard haemodynamic response kernels on simulated data, and perform a full analysis of data acquired during an experiment involving visual stimulation.     

Separation of Non-Negative Mixture of Non-Negative Sources Using a Bayesian Approach and MCMC Sampling

This paper addresses blind-source separation in the case where both the source signals and the mixing coefficients are non-negative. The problem is referred to as non-negative source separation and the main application concerns the analysis of spectrometric data sets. The separation is performed in a Bayesian framework by encoding non-negativity through the assignment of Gamma priors on the distributions of both the source signals and the mixing coefficients. A Markov chain Monte Carlo (MCMC) sampling procedure is proposed to simulate the resulting joint posterior density from which marginal posterior mean estimates of the source signals and mixing coefficients are obtained. Results obtained with synthetic and experimental spectra are used to discuss the problem of non-negative source separation and to illustrate the effectiveness of the proposed method.     

Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing

Bayesian compressive sensing (CS) is considered for signals and images that are sparse in a wavelet basis. The statistical structure of the wavelet coefficients is exploited explicitly in the proposed model, and, therefore, this framework goes beyond simply assuming that the data are compressible in a wavelet basis. The structure exploited within the wavelet coefficients is consistent with that used in wavelet-based compression algorithms. A hierarchical Bayesian model is constituted, with efficient inference via Markov chain Monte Carlo (MCMC) sampling. The algorithm is fully developed and demonstrated using several natural images, with performance comparisons to many state-of-the-art compressive-sensing inversion algorithms.      

An MCMC sampling approach to estimation of nonstationary hiddenMarkov models

Hidden Markov models (HMMs) represent a very important tool for analysis of signals and systems. In the past two decades, HMMs have attracted the attention of various research communities, including the ones in statistics, engineering, and mathematics. Their extensive use in signal processing and, in particular, speech processing is well documented. A major weakness of conventional HMMs is their inflexibility in modeling state durations. This weakness can be avoided by adopting a more complicated class of HMMs known as nonstationary HMMs. We analyze nonstationary HMMs whose state transition probabilities are functions of time that indirectly model state durations by a given probability mass function and whose observation spaces are discrete. The objective of our work is to estimate all the unknowns of a nonstationary HMM, which include its parameters and the state sequence. To that end, we construct a Markov chain Monte Carlo (MCMC) sampling scheme, where sampling from all the posterior probability distributions is very easy. The proposed MCMC sampling scheme has been tested in extensive computer simulations on finite discrete-valued observed data, and some of the simulation results are presented     

Analytical framework for power saving evaluation in two-tier heterogeneous mobile networks

Reducing the power consumption of base stations in mobile networks is an important issue. We investigate the power saving evaluation in two-tier heterogeneous mobile networks which consist of femtocells overlaid by macrocells. In the heterogeneous mobile networks, base stations without traffic load are allowed to enter the sleep mode to save power. The power saving probability that a base station enters the sleep mode and the average total power consumption of this network are complex joint-effects of various factors. Successful modelling of these complex joint-effects is critical to mobile network operators when they pursue the design of green mobile networks. In this paper we propose an analytical framework to facilitate systematic analysis. Based on the proposed analytical framework, we investigate the power saving probabilities and the average total power consumption in terms of several parameters, including the new traffic arrival rate per user, the maximum transmission power of a femtocell, the number of femtocells within a macrocell, and the number of users in the network. Numerical results show that the proposed analytical framework provides a useful and efficient method to facilitate systematic analysis and design of green mobile networks. Simulation results validate the accuracy of the proposed analytical framework.     

Enhanced sampling schemes for MCMC based blind Bernoulli-Gaussian deconvolution

This paper proposes and compares two new sampling schemes for sparse deconvolution using a Bernoulli-Gaussian model. To tackle such a deconvolution problem in a blind and unsupervised context, the Markov Chain Monte Carlo (MCMC) framework is usually adopted, and the chosen sampling scheme is most often the Gibbs sampler. However, such a sampling scheme fails to explore the state space efficiently. Our first alternative, the K-tuple Gibbs sampler, is simply a grouped Gibbs sampler. The second one, called partially marginalized sampler, is obtained by integrating the Gaussian amplitudes out of the target distribution. While the mathematical validity of the first scheme is obvious as a particular instance of the Gibbs sampler, a more detailed analysis is provided to prove the validity of the second scheme.For both methods, optimized implementations are proposed in terms of computation and storage cost. Finally, simulation results validate both schemes as more efficient in terms of convergence time compared with the plain Gibbs sampler. Benchmark sequence simulations show that the partially marginalized sampler takes fewer iterations to converge than the K-tuple Gibbs sampler. However, its computation load per iteration grows almost quadratically with respect to the data length, while it only grows linearly for the K-tuple Gibbs sampler.     

基于DSP的无功补偿控制系统采样电路的设计

针对目前配网中电压与电流较难实时测量的问题,详细介绍了MCR的无功补偿控制系统采样电路的设计思想,设计出基于DSP采样的硬件电路,对硬件电路设计上进行了优化,对硬件的可靠性详细的分析。由于滤波以及触发信号的时间基准问题,同时也设计了相位补偿硬件电路以及过零检测硬件电路。实验结果表明采样电路、相位补偿电路和过零检测电路解决了MCR的无功补偿控制系统中电压、电流采样问题以及晶闸管触发信号的时间基准问题。