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Research on seamless INS/GPS integrated navigation algorithm

  • Tianlai Xu*
  • , Yang Tian
  • *Corresponding author for this work
  • Harbin Institute of Technology

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

Abstract

This paper proposed and discussed an INS/GPS integrated navigation method based on radial basis function neural network (RBFNN) to fuse INS and GPS data. When GPS signals were available, an adaptive Kalman filter was used to improve the estimation accuracy of INS errors, and then the RBFNN structure was trained to mimic the dynamical error model of INS. If GPS signals were unavailable, the trained RBFNN structure was utilized to bridge the GPS outages to achieve seamless navigation. Simulations in INS/GPS integrated navigation system showed the proposed method can reduce the positioning error during GPS outages.

Original languageEnglish
Title of host publicationMaterials and Manufacturing
Pages1178-1181
Number of pages4
DOIs
StatePublished - 2011
EventInternational Conference on Material and Manufacturing, ICMM 2011 - Jinzhou, Liaoning, China
Duration: 7 Sep 20119 Sep 2011

Publication series

NameAdvanced Materials Research
Volume299-300
ISSN (Print)1022-6680

Conference

ConferenceInternational Conference on Material and Manufacturing, ICMM 2011
Country/TerritoryChina
CityJinzhou, Liaoning
Period7/09/119/09/11

Keywords

  • INS
  • Integrated navigation system
  • Kalman filter
  • RBFNN

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