State estimation for flag Hidden Markov Models with imperfect sensors
State estimation for flag Hidden Markov Models with imperfect sensors
复制标题
具有不完善传感器的标志隐马尔可夫模型的状态估计
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
T. Fischer
中科院分区：
文献类型：
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作者：
Kyle Doty;Sandip Roy;T. Fischer
研究了一类特殊的标志隐马尔可夫模型（hmm）的状态检测问题，该模型包括：1)一个任意有限状态的底层马尔可夫链；2)一个结构化的观测过程，其中一个子集的状态以一定的概率发出不同的标志，而其他状态是不可测量的。本文的重点是开发最大似然滤波器的显式误差概率计算，特别是在传感器不完美的情况下。在几个示例中，利用代数结果来解决传感器的放置问题，其中一个示例是根据现场数据绘制的家庭环境中的活动监控。
State detection is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structured observation process wherein a subset of states emit distinct flags with some probability while other states are unmeasured. The focus of this article is to develop an explicit computation of the probability of error for the maximum-likelihood filter, specifically for the case that the sensors are imperfect. The algebraic result is leveraged to address sensor placement in a couple of examples, including one on activity-monitoring in a home environment that is drawn from field data.