State Estimation for Flag Hidden Markov Models with Imperfect Sensors

被引：0

作者：

Doty, Kyle ^{[1
]}

Roy, Sandip ^{[1
]}

Fischer, Thomas R. ^{[1
]}

机构：

[1] Washington State Univ, Pullman, WA 99164 USA

来源：

2016 ANNUAL CONFERENCE ON INFORMATION SCIENCE AND SYSTEMS (CISS) | 2016年

关键词：

Hidden Markov Models; Smart Homes; Maximum Likelihood; PROBABILITY; BOUNDS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：6

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