Consensus-Based State Estimation over Sensor Networks with Correlated Noise

Wenbiao Ge, Jialing Zhou, Mengfei Niu

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

Abstract

In this paper, the problem of state estimation over sensor networks for discrete-time linear time-varying systems with correlated noise is studied. Herein, we consider that the measurement noise of each sensor is cross-correlated with the process noise, and the measurement noise of different sensors are also cross-correlated. To estimate the system state in such a case, a consensus-based state estimation algorithm is designed. The effectiveness of the proposed filtering algorithm is further demonstrated by comparative simulations.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages964-969
Number of pages6
ISBN (Electronic)9780738146577
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Unmanned Systems, ICUS 2021 - Beijing, China
Duration: 15 Oct 202117 Oct 2021

Publication series

NameProceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021

Conference

Conference2021 IEEE International Conference on Unmanned Systems, ICUS 2021
Country/TerritoryChina
CityBeijing
Period15/10/2117/10/21

Keywords

  • Kalman consensus filter(KCF)
  • correlated noise
  • state estimation

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