Kalman/particle filter for integrated navigation system - 文章详情页

作者：

Cui, P.-Y. (Cui, P.-Y..) | Zheng, L.-F. (Zheng, L.-F..) | Pei, F.-J. (Pei, F.-J..) | Liu, H.-Y. (Liu, H.-Y..)

收录：

Scopus PKU CSCD

摘要：

Particle　filter　is　a　best　way　for　the　state　estimation　of　highly　nonlinear　integrated　navigation　systems　with　non-Gaussian　uncertainties.　Since　the　error　model　of　SINS　has　high　dimensions,　traditional　particle　filter　would　bring　hard　computation.　A　new　Kalman/Particle　mixed　filter　used　on　SINS/GPS　integrated　navigation　system　was　proposed.　The　new　method　divides　the　system　into　two　sub-models,　one　is　linear,　the　other　one　is　nonlinear,　and　then　implement　Kalman　filter　and　particle　filter　separately.　Residual-systematic　resampling　and　regularized　algorithms　were　involved　to　decrease　particle　filter＇　particles　collapse　weakness　and　hard　computation.　The　simulation　results　show　that　their　performance　is　almost　equal,　but　the　computation　complexity　of　the　Kalman/particle　filter　is　much　lower　than　traditional　particle　filter.

关键词：

Integrated navigation system; Kalman/particle filter; Particle filter; Strap-down inertial navigation system

作者机构：

[ 1 ] [Cui, P.-Y.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 2 ] [Zheng, L.-F.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 3 ] [Pei, F.-J.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 4 ] [Liu, H.-Y.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China

通讯作者信息：

[Cui, P.-Y.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China

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