Hidden Markov model state estimation with randomly delayed observations
Hidden Markov model state estimation with randomly delayed observations
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DOI：
10.1109/78.774757
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发表时间：
1999-08-01
影响因子：
5.4
通讯作者：
Krishnamurthy, V
中科院分区：
文献类型：
--
作者：
Evans, JS;Krishnamurthy, V
本文考虑离散时间隐马尔可夫模型（HMM）的状态估计，当观测值被随机延迟时，延迟过程本身被建模为有限状态马尔可夫链，允许增广状态HMM对整个系统进行建模。状态估计算法的HMM，然后提出，并在仿真中研究其性能。该模型的动机源于分布式传感器通过无连接的分组交换通信网络传输测量结果的情况。
This paper considers state estimation for a discrete-time hidden Markov model (HMM) when the observations are delayed by a random time, The delay process is itself modeled as a finite state Markov chain that allows an augmented state HMM to model the overall system. State estimation algorithms for the resulting HMM are then presented, and their performance is studied in simulations. The motivation for the model stems from the situation when distributed sensors transmit measurements over a connectionless packet switched communications network.