Linear minimum mean square error estimation for linear systems with stochastic state equality constraints

Yanbo Yang, Quan Pan, Yan Liang, Feng Yang

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

Abstract

In the practical linear system with the state equality constraint, the constraint may exist stochastically. Considered this phenomenon, the stochastic distribution of the state equality constraint is modeled as the Bernoulli distribution, and regarded it as the perfect measurement drew into the measurement equation in this paper. A tiny noise may be subjoined on it to avoid leading the covariance of the augmented measurement noise singular. At this time, the original system can be generalized to the linear system with a part of its measurement received stochastically. Aimed at this system, the linear minimum mean square error estimator (LMMSE) is derived according to the orthogonality principle. The simulation result shows that the proposed method has a same performance with the traditional method which is used to deal with the linear system with the state equality constraint when the constraint is existed all the time. However, the performance of the proposed method is prior to that of the traditional method when the state equality constraint is existed stochastically.

Original languageEnglish
Title of host publicationProceedings of the 32nd Chinese Control Conference, CCC 2013
PublisherIEEE Computer Society
Pages4747-4752
Number of pages6
ISBN (Print)9789881563835
StatePublished - 18 Oct 2013
Event32nd Chinese Control Conference, CCC 2013 - Xi'an, China
Duration: 26 Jul 201328 Jul 2013

Publication series

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

Conference

Conference32nd Chinese Control Conference, CCC 2013
Country/TerritoryChina
CityXi'an
Period26/07/1328/07/13

Keywords

  • Bernoulli distribution
  • linear minimum mean square error estimator
  • linear system
  • state equality constraint

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