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Levenberg-Marquardt Optimization Based Iterative Divided Difference Filtering for Multi-AUV Cooperative Localization

  • Sun Chengjiao
  • , Zhang Yonggang
  • , Gao Wei
  • , Luo Li
  • , Jia Guangle
  • Harbin Engineering University

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

Abstract

Due to the low communication frequency and slow transmission speed of underwater acoustic communication, the frequency of cooperative update is significantly reduced, which results in poor filtering convergence and positioning accuracy. To solve this problem, in this paper, a new iterative divided difference filter (IDDF) is proposed for multi-AUV cooperative localization system based on a Levenberg-Marquardt optimization. As compared with conventional filtering algorithm, the proposed IDDF algorithm can improve the convergence and estimation accuracy when the state estimation has large initialization error. Simulation results show that the proposed cooperative localization algorithm has better filtering convergence and positioning accuracy then existing methods.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages4164-4168
Number of pages5
ISBN (Electronic)9789881563941
DOIs
StatePublished - 5 Oct 2018
Externally publishedYes
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 25 Jul 201827 Jul 2018

Publication series

NameChinese Control Conference, CCC
Volume2018-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference37th Chinese Control Conference, CCC 2018
Country/TerritoryChina
CityWuhan
Period25/07/1827/07/18

Keywords

  • Autonomous underwater vehicles
  • Cooperative localization
  • Divided difference filter
  • Nonlinear filtering

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