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针对子系统发生缓变故障会影响联邦滤波器精度的问题,分析了信息分配因子对子滤波器精度和鲁棒性、全局估计精度以及故障检测效率的影响.在此基础上,研究了一种自适应容错联邦滤波方案.通过对量测噪声阵进行自适应调节来降低未检测出来的故障信息对故障子滤波器和全局估计精度的影响,进而提升无故障子滤波器的精度和系统重构能力;根据子滤波器故障检测函数值来动态调节信息分配因子,可进一步提升故障检测效率.仿真结果表明,相比于传统的容错联邦滤波,该方法能有效降低故障信息对滤波精度的影响,具有较高的全局估计精度. 相似文献
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故障条件下的联邦滤波鲁棒信息分配方法研究 总被引:3,自引:1,他引:2
研究了某个子系统发生传感器故障时,信息分配对无故障子系统的鲁棒性影响问题.基于提高无故障子系统的抗故障污染能力考虑,从使故障影响极小化出发提出了一种鲁棒信息分配思想.对于仅由双子滤波器组成的联邦滤波结构,给出了一种基于故障检测函数的自适应信息分配方法.从概率的角度,根据这种方法可使其他无故障子滤波器具有较强的抗干扰能力.对于具有主滤波器的联邦滤波结构,给出了一种给定故障衰减系数条件下的鲁棒信息分配系数设计方法.理论分析表明,该方法可使其他无故障子滤波器对故障具有很强的抗干扰能力,从而有利于提高联邦滤波器的快速重构能力.仿真说明本文思想是可行的. 相似文献
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针对联邦滤波子系统软故障难以检测问题,提出一种基于滑动状态递推器的改进B型灰色关联度检测法;采用具有最高容错性能的无反馈模式,克服交叉污染;引入了自适应权衡因子,并给出计算公式,权衡无反馈模式下的最优信息分配和容错信息分配。根据所提算法,联邦滤波器信息分配系数可根据故障程度自动调整,并在权衡因子的作用下,保证滤波全程实现接近最优融合精度。该算法具有计算量小,结构简洁,精度高的特点,适合实际应用。该例仿真显示在故障最大时全局滤波精度提高28.5%左右,表明该算法有效提高了故障条件下全局融合精度,并实现全程接近最优融合精度。 相似文献
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针对联邦滤波器子系统同时存在硬故障和软故障问题,提出一种适用于联邦滤波结构的两级故障检测方法。首先,构造联邦结构残差2χ检验法对系统硬故障进行检测,再用第k-m步未发生故障时的全局最优估计信息构造滑动残差检验函数,对未检测出的软故障进行时间积累,进而检测软故障,同时,联邦滤波信息分配系数根据软故障检测函数进行自适应调节。通过SINS-Galileo-北斗组合导航系统仿真对比分析了基于局部滤波残差2χ检验法和本文提出的故障检测方法,结果表明:该故障检测方法对系统硬故障和软故障具有较高的故障检测灵敏度,能够提高组合导航系统的可靠性。 相似文献
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基于模糊自适应Kalman滤波的GPS/DR数据融合 总被引:2,自引:0,他引:2
针对标准Kalman滤波器对系统模型依赖性强、鲁棒性差,而GPS/DR系统的准确数学模型难以建立的问题,提出了一种模糊自适应联邦卡尔曼滤波器(FAFKF).首先通过模糊自适应滤波控制器监控观测量的残差理论值和实际值,并通过实时增强它们的一致性来调整各子系统观测噪声方差阵,使之更符合真实的模型,有效提高了Kalman滤波器对模型变化的适应能力.然后通过模糊自适应信息融合控制器对各子系统可信度进行模糊评判,并根据可信度自适应地计算信息分配系数来实现数据的融合.理论分析和实验数据表明该滤波器在滤波精度、容错性能上都有了很大的提高. 相似文献
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研究了一种基于动态扰动的滤波算法,用以提高动态扰动情况下捷联惯导/多卫星组合导航系统
的精度和可靠性.该算法采用几何精度因子(GDOP)对量测噪声进行自适应调节,利用卡尔曼滤波器的新息
量对状态噪声协方差阵进行整体控制,同时根据具有时变特性的各子系统误差协方差阵对信息分配系数进行
自适应调节.通过对SINS/GPS/Galileo/北斗组合导航系统的仿真,分析对比了常规联邦滤波、Sage 自适应联邦
滤波和本文所提自适应联邦滤波算法.结果表明,该自适应联邦滤波算法能够有效抑制动态扰动,提高组合
导航系统的精度和可靠性. 相似文献
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电脑操作中80%以上的问题是软件引起的,计算机故障尽管五花八门、千奇百怪,但由于计算机是由一种逻辑部件构成的电子装置,所以软件故障诊断的基本原则,软件故障诊断的方法,计算机软件故障的检修流程,计算机软件故障快速修复的常用方法是有规律可循,可以梳理总结出来。掌握这些规律,计算机软件故障修复可快速解决。 相似文献
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网络故障极为繁杂,也相当普遍。如果把网络故障的常见故障进行归类查找,那么无疑能够迅速而准确地查找故障根源,解决网络故障。文章论述了常见网络故障的分析及排除。 相似文献
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A Survey of Fault Management in Wireless Sensor Networks 总被引:4,自引:0,他引:4
Wireless sensor networks are resource-constrained self-organizing systems that are often deployed in inaccessible and inhospitable
environments in order to collect data about some outside world phenomenon. For most sensor network applications, point-to-point
reliability is not the main objective; instead, reliable event-of-interest delivery to the server needs to be guaranteed (possibly
with a certain probability). The nature of communication in sensor networks is unpredictable and failure-prone, even more
so than in regular wireless ad hoc networks. Therefore, it is essential to provide fault tolerant techniques for distributed
sensor applications. Many recent studies in this area take drastically different approaches to addressing the fault tolerance
issue in routing, transport and/or application layers. In this paper, we summarize and compare existing fault tolerant techniques
to support sensor applications. We also discuss several interesting open research directions.
Lilia Paradis is currently a graduate student in the Department of Mathematical and Computer Sciences, Colorado School of Mines. She is
also part of the Toilers Ad Hoc Networking research group. She is interested in distributed communication protocols for wireless
sensor networks.
Qi Han received the PhD degree in computer science from the University of California, Irvine in 2005. She is currently an assistant
professor in the Department of Mathematical and Computer Sciences, Colorado School of Mines. Her research interests include
distributed systems, middleware, mobile and pervasive computing, systems support for sensor applications, and dynamic data
management. She is specifically interested in developing adaptive middleware techniques for next generation distributed systems.
She is a member of the IEEE and the ACM. 相似文献
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An important aspect of network management is fault management, which involves, detecting, locating, isolating, correcting and adapting to faults in the network. We study modeling of communication network protocol and fault detection, identification and localization in the discrete event system diagnosis framework. As an illustration of the approach, normal and faulty behavior of the X.25 network protocol is modeled as a finite state machine. This modeling formalism allows the utilization of discrete event system analysis for the detection and diagnosis of faults. Our approach provides a systematic way of performing fault diagnosis for network fault management. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society 相似文献
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