Joint estimation of state and bias based on generalized systematic model

Jie Zhou, Yan Liang, Lin Zhou, Quan Pan

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

2 Scopus citations

Abstract

This paper presents a joint estimation of state and bias based on generalized systematic model. Registration process is implemented as follows: first of all, augment method is utilized to derive dynamic equation of the system. Then, structure unknown inputs induced by the dynamic equation of the bias are decoupled. Unbiased estimation of the state and bias is finally obtained by the augment minimum mean squared estimation (AMMSE). The simulation proves that the proposed method is not only effective but also efficient by comparing with other methods, respectively.

Original languageEnglish
Title of host publicationProceedings of the 34th Chinese Control Conference, CCC 2015
EditorsQianchuan Zhao, Shirong Liu
PublisherIEEE Computer Society
Pages4750-4755
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

  • AMMSE
  • Generalized model
  • Joint estimation
  • Sensor registration
  • Systematic bias

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