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Sequential fusion state estimation algorithm for multi-sensor nonlinear systems with cross-correlated noise

  • Harbin Institute of Technology
  • Beijing Institute of Astronautical Systems Engineering

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

Abstract

This paper is concerned the problem of multi-sensor state estimation with cross-correlated noise, this paper adopts sequential fusion to estimate the state. The statistical characteristics of measurement noise of different sensors in the multi-sensor system is related, and also related to the system noise in one step. Firstly, based on the estimation of observation noise, a global optimal fusion filter based on sequential fusion and Cubature Kalman filter is proposed for the first time. Secondly, the algorithm proposed in this paper is simulated by numerical method. The method of numerical realization is Cubature Kalman filter based on deterministic sampling. Finally, the effectiveness of the proposed algorithm is demonstrated by a simulation example.

Original languageEnglish
Title of host publicationProceedings - 2022 Chinese Automation Congress, CAC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1617-1621
Number of pages5
ISBN (Electronic)9781665465335
DOIs
StatePublished - 2022
Event2022 Chinese Automation Congress, CAC 2022 - Xiamen, China
Duration: 25 Nov 202227 Nov 2022

Publication series

NameProceedings - 2022 Chinese Automation Congress, CAC 2022
Volume2022-January

Conference

Conference2022 Chinese Automation Congress, CAC 2022
Country/TerritoryChina
CityXiamen
Period25/11/2227/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • correlated noise
  • noise estimation
  • nonlinear system
  • sequential fusion
  • state estimation

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