文献类型：期刊文献

英文题名：Event-triggered risk-sensitive state estimation for hidden markov models

作者：Xu, Jiapeng[1]; Ho, Daniel W.C.[2]; Li, Fangfei[3]; Yang, Wen[1]; Tang, Yang[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong; [3] Department of Mathematics, East China University of Science and Technology, Shanghai, 200237, China

年份：2019

卷号：64

期号：10

起止页码：4276

外文期刊名：IEEE Transactions on Automatic Control

收录：EI(收录号：20194807754598)

语种：英文

外文关键词：Probability distributions - Risk perception - Trellis codes - State estimation

摘要：An event-triggered risk-sensitive state estimation problem for hidden Markov models is investigated in this work. The event-triggered scheme considered is fairly general, which covers most existing event-triggered conditions. By utilizing the reference probability measure approach, this estimation problem is reformulated as an equivalent one and solved. We show that the event-triggered risk-sensitive maximum a posteriori probability estimates can be obtained based on a newly defined unnormalized information state, which has a linear recursive form. Furthermore, the explicit solutions for two major classes of event-triggered conditions are derived if the measurement noise is Gaussian. A numerical comparison is provided to illustrate the effectiveness of the proposed results. ? 2019 IEEE.