文献类型：期刊文献

英文题名：Even 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]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China;[3]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R 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：;WOS:【SCI-EXPANDED(收录号:WOS:000490788900001)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0809302, in part by the National Natural Science Foundation of China under Grant 61751305, Grant 61673176, Grant 61773161, and Grant 61573143, in part by Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant CityU 11300415 and Grant CityU 11200717, in part by the Natural Science Foundation of Shanghai Under Grant 18ZR1409800 and Grant 18ZR1409700, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017. Recommended by Associate Editor K. Kashima. (Corresponding author: Yang Tang.)

语种：英文

外文关键词：Event-triggered state estimation; hidden Markov models (HMMs); maximum a posteriori probability (MAP); risk sensitive

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.