Square-root unscented Kalman filter for vehicle integrated navigation - Details

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：

EI Scopus

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.　©2007　IEEE.

Keyword：

Extended Kalman filters State estimation Navigation systems Computer simulation Data fusion Nonlinear filtering Global positioning system

Author Community：

[ 1 ] [Zhang, Li-Guo]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 2 ] [Ma, Hai-Bo]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 3 ] [Chen, Yang-Zhou]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China