The LMMSE estimation for Markovian jump linear systems with stochastic coefficient matrices and one-step randomly delayed measurements

Yuemei Qin, Yan Liang, Yanbo Yang, Yanting Yang, Quan Pan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents the state estimation problem of discrete-time Markovian jump linear systems (MJLSs) with stochastic coefficient matrices (SCMs) and one-step randomly delayed measurements (RODs). Here, the SCMs are modeled as the randomly weighted sum of a series known basis matrices while the RODs are represented by a sequence of independent Bernoulli random variables. The proposed system is the MJLS with multiple stochastic parameters, including stochastic system matrices leading the uncertainty coupling between system matrices and state/noises, and ranndom Bernoulli variables leading the real measurement correlated with that at previous instant. By geometry augmentation, the state coupled with mode uncertainty is estimated instead of estimating the original state directly. Then, the linear minimum-mean-square error (LMMSE) estimator is derived in a recursive structure according to the orthogonality principle. A numerical simulation is presented to testify the proposed method.

Original languageEnglish
Title of host publicationProceedings of the 34th Chinese Control Conference, CCC 2015
EditorsQianchuan Zhao, Shirong Liu
PublisherIEEE Computer Society
Pages4783-4788
Number of pages6
ISBN (Electronic)9789881563897
DOIs
StatePublished - 11 Sep 2015
Event34th Chinese Control Conference, CCC 2015 - Hangzhou, China
Duration: 28 Jul 201530 Jul 2015

Publication series

NameChinese Control Conference, CCC
Volume2015-September
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference34th Chinese Control Conference, CCC 2015
Country/TerritoryChina
CityHangzhou
Period28/07/1530/07/15

Keywords

  • LMMSE
  • Markovian jump linear system
  • one-step delay
  • stochastic coefficient matrix

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