An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems

被引：5

作者：

Cheng, Jianhua ^{[1
,2
]}

Chen, Daidai ^{[1
]}

Landry, Rene Jr ^{[2
]}

Zhao, Lin ^{[1
]}

Guan, Dongxue ^{[1
]}

机构：

[1] Harbin Engn Univ, Marine Nav Res Inst, Coll Automat, Harbin 150001, Peoples R China

[2] Univ Quebec, Ecole Technol Super, LASSENA Lab, Montreal, PQ H3C 1K3, Canada

来源：

JOURNAL OF APPLIED MATHEMATICS | 2014年

关键词：

INS/GPS;

D O I：

10.1155/2014/451939

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF) and unscented Kalman filtering (AUKF) algorithms, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively compensate the time-varying noise characteristics. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained. Simulations are conducted for MEMS/GPS integrated navigation system. The results show that the performance of estimation is improved by the AUKF approach compared with both conventional AKF and UKF.

引用

页数：8

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