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Improved Embedded Cubature Kalman Algorithm in Cooperative Navigation

  • Harbin Institute of Technology Weihai

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

Abstract

For the issues of non-additive system noise and time-varying measurement noise during the cooperative navigation model of multiple AUVs, this paper proposes a square-root decomposition-based Extended dimension Cubature Kalman Filtering (SRECKF) algorithm. Firstly, the multiplicative noise is incorporated into the state variables for estimation, leveraging the model to estimate the noise. Subsequently, the square-root decomposition replaces the original Cholesky decomposition, propagating the square root of the variance. Finally, simulation experiments demonstrate that the proposed algorithm offers higher localization accuracy and stability compared to the Embedded Cubature Kalman Filter (ECKF) and the Extended Dimension Cubature Kalman Filter based on the SVD decomposition algorithm (SVDECKF).

Original languageEnglish
Title of host publicationAdvances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 9
EditorsLiang Yan, Haibin Duan, Yimin Deng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages113-116
Number of pages4
ISBN (Print)9789819622313
DOIs
StatePublished - 2025
Externally publishedYes
EventInternational Conference on Guidance, Navigation and Control, ICGNC 2024 - Changsha, China
Duration: 9 Aug 202411 Aug 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1345 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Guidance, Navigation and Control, ICGNC 2024
Country/TerritoryChina
CityChangsha
Period9/08/2411/08/24

Keywords

  • Cubature Kalman Filter
  • Embedded cubature criterion
  • Multiplicative noise
  • Square-root Decomposition
  • Time-varying noise

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