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排序方式: 共有218条查询结果,搜索用时 15 毫秒
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Alexander G. Tartakovsky 《Sequential Analysis》2013,32(4):441-475
Abstract In the standard formulation of the quickest change-point detection problem, a sequence of observations, whose distribution changes at some unknown point in time, is available to a decision maker, and the goal is to detect this change as quickly as possible, subject to false alarm constraints. In this paper, we study the quickest change detection problem in the setting where the information available for decision-making is distributed across a set of geographically separated sensors, and only a compressed version of observations in sensors may be used for final decision-making due to communication bandwidth constraints. We consider the minimax, uniform, and Bayesian versions of the optimization problem, and we present asymptotically optimal decentralized quickest change detection procedures for two scenarios. In the first scenario, the sensors send quantized versions of their observations to a fusion center where the change detection is performed based on all the sensor messages. In the second scenario, the sensors perform local change detection and send their final decisions to the fusion center for combining. We show that our decentralized procedures for the latter scenario have the same first-order asymptotic performance as the corresponding centralized procedures that have access to all of the sensor observations. We also present simulation results for examples involving Gaussian and Poisson observations. These examples show that although the procedures with local decisions are globally asymptotically optimal as the false alarm rate (or probability) goes to zero, they perform worse than the corresponding decentralized procedures with binary quantization at the sensors, unless the false alarm rate (or probability) is unreasonably small. 相似文献
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Michael Baron 《Sequential Analysis》2013,32(1):15-18
Abstract Motivated by Professor Siegmund's article and his other works in the area of change-point analysis and its biomedical applications, we discuss approaches to change-point–related problems that involve parameters of unknown dimension. 相似文献
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AbstractOptimality properties of decision procedures are studied for the quickest detection of a change-point of parameters in autoregressive and other Markov type sequences. The limit of the normalized conditional log-likelihood ratios is shown to exist for Markov chains satisfying the ergodic theorem of information theory. Then closed-form expressions for this limit are derived by making use of the time average rate of Kullback-Leibler divergence. The good properties of the detection procedures based on a sequential analysis approach are proven to hold thanks to geometric ergodicity properties of the observation processes. In particular, the window-limited CUSUM rule is shown to be optimal for detecting the disruption point in autoregressive models. Sparre Andersen models are specifically studied. 相似文献
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论述了多元累积和控制图的有关内容,提出了2种质量多元累积和控制图的诊断理论,并与多元T^2控制图进行了比较,最后通过1个实例对诊断理论的应用加以说明。 相似文献
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累积和控制图监控过程均值偏移的应用较多,而用于监控过程变差较少。在机械零、部件的大批量、高精度生产过程中,需要同时监控过程均值和变差。在介绍三种主要的监控变差的CUSUM控制图方法基础上,结合案例给予比较,并指出各自的不足之处。 相似文献
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提出了一种新的认知超宽带频谱感知算法,该算法适合检测低平均信噪比、低占空比的超宽带脉冲信号,且具有复杂度低、检测延迟性能优越的特点.论文根据超宽带脉冲信号特性,给出形式统一的低占空比接收信号概率密度函数表达式,并采用累加和(CUSUM)算法实施检测.仿真结果表明,该算法对低占空比信号具有良好的适应性,有效克服了能量检测的信噪比门限效应,且检测延迟性能优越. 相似文献