Unscented Kalman filter for DR/GPS integrated navigation system

Yongqiang Han*, Jiabin Chen, Zhide Liu, Dunhui Zhao, Chunlei Song, Jingyuan Yin

*Corresponding author for this work

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

Abstract

In this paper, unscented Kalman filter (UKF) is studied to estimate the state of Dead reckoning (DR) and GPS integrated navigation system. The positioning error of DR system mainly comes from two factors, the azimuth error and odometer scale factor error. Conventional model of DR/GPS integrated navigation chooses acceleration, position and velocity as observation states, and azimuth error is not estimated, which is one of the key error sources. A new error model of DR/GPS system is adopted that includes azimuth error as a state, which makes it possible to estimate both of the two major error sources. UKF directly approximates the probability density distribution of random variable and avoids the linearization of nonlinear function, which improves the filtering precision. UKF is used to implement an improved DR/GPS system. Road test has been conducted to prove the effectiveness of the scheme.

Original languageEnglish
Title of host publicationNanotechnology and Computer Engineering
Pages750-755
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 IITA International Conference on Nanotechnology and Computer Engineering, CNCE 2010 - Qingdao, China
Duration: 20 Jul 201021 Jul 2010

Publication series

NameAdvanced Materials Research
Volume121-122
ISSN (Print)1022-6680

Conference

Conference2010 IITA International Conference on Nanotechnology and Computer Engineering, CNCE 2010
Country/TerritoryChina
CityQingdao
Period20/07/1021/07/10

Keywords

  • Azimuth error
  • Dead reckoning
  • Integrated navigation system
  • Unscented Kalman filter

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