Data fusion with packet loss

Xiaolei Bian*, Yuanqing Xia, Liping Yan, Mengyin Fu

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

This paper studies the stability of state estimation for a discrete-time linear stochastic system, the states of which are measured by multiple sensors and transmitted over multiple wireless channels. Random packet loss process introduced by each wireless channel is modeled by an independent and identically distributed (i.i.d.) Bernoulli process. The estimation strategy designed in this paper is based on Covariance Intersection fusion of local state estimates of the observable subsystem of each sensor. The sufficient conditions, imposing constraint on the packet success probability of each channel, are established by taking into account each observable subsystem structure to guarantee the expectation of the trace of estimation error covariance matrices is exponentially bounded, and the upper bound is given. Simulation examples are provided to demonstrate the effectiveness of the results.

Original languageEnglish
Title of host publicationProceedings of the 33rd Chinese Control Conference, CCC 2014
EditorsShengyuan Xu, Qianchuan Zhao
PublisherIEEE Computer Society
Pages7409-7414
Number of pages6
ISBN (Electronic)9789881563842
DOIs
Publication statusPublished - 11 Sept 2014
EventProceedings of the 33rd Chinese Control Conference, CCC 2014 - Nanjing, China
Duration: 28 Jul 201430 Jul 2014

Publication series

NameProceedings of the 33rd Chinese Control Conference, CCC 2014
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

ConferenceProceedings of the 33rd Chinese Control Conference, CCC 2014
Country/TerritoryChina
CityNanjing
Period28/07/1430/07/14

Keywords

  • Covariance Intersection
  • Data Fusion
  • Kalman Filtering
  • Packet loss

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