Joint estimation and identification for stochastic systems with two kinds of unknown inputs

Hua Lan, Yan Liang, Feng Yang, Quan Pan

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

Abstract

This paper discusses the joint estimation and identification of a class of discrete-time stochastic systems with two kinds of unknown inputs (UIs). A general framework of the stochastic system model with two kinds of UIs is built. Furthermore, a new joint estimation and identification method is proposed for dealing with the problem via iterative optimization. An numerical example of tracking a maneuvering target accompanied range gate pull-off(RGPO) is utilized to illustrate the proposed method and its performance in parameter identification and state estimation.

Original languageEnglish
Title of host publicationProceedings of the 32nd Chinese Control Conference, CCC 2013
PublisherIEEE Computer Society
Pages1723-1728
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

  • iterative optimization
  • Joint estimation and identification
  • unknown inputs

Fingerprint

Dive into the research topics of 'Joint estimation and identification for stochastic systems with two kinds of unknown inputs'. Together they form a unique fingerprint.

Cite this