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Author:

Zhang, Li-Guo (Zhang, Li-Guo.) (Scholars:张利国) | Ma, Hai-Bo (Ma, Hai-Bo.) | Chen, Yang-Zhou (Chen, Yang-Zhou.) (Scholars:陈阳舟)

Indexed by:

CPCI-S

Abstract:

In view of that there exist some defects when the extended Kalman filter (EKF) is applied in nonlinear state estimations, The Square-Root Unscented Kalman Filter (SRUKF), as a new nonlinear filtering method, is introduced to instead of EKF for the state-estimation of the vehicle integrated GPS/DR navigation system. Compared with EKF, SRUKF not only improves the location precision and algorithmic stability greatly, but also avoids the calculating burden of Jacobin matrices. This data fusion algorithm based on SRUKF is easy to implement, and meets the requirements of low-cost and high precision. In order to test the validity of SRUKF, the two methods are used to estimate states of the vehicle integrated GPS/DR navigation systems. The results of simulation show that SRUKF is superior to EKF and is a more ideal nonlinear filtering method for the vehicle integrated GPS/DR navigation.

Keyword:

EKF SRUKF GPS/DR vehicle navigation

Author Community:

  • [ 1 ] [Zhang, Li-Guo]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100022, Peoples R China
  • [ 2 ] [Ma, Hai-Bo]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100022, Peoples R China
  • [ 3 ] [Chen, Yang-Zhou]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100022, Peoples R China

Reprint Author's Address:

  • 张利国

    [Zhang, Li-Guo]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100022, Peoples R China

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Source :

PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7

Year: 2007

Page: 556-561

Language: English

Cited Count:

WoS CC Cited Count: 8

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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