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

Jianhua Cheng | Zendy; Daidai Chen | Zendy; René Landry | Zendy; Lin Zhao | Zendy; Dongxue Guan | Zendy

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

Author(s) -

Jianhua Cheng,

Daidai Chen,

René Landry,

Lin Zhao,

Dongxue Guan

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/451939

Subject(s) - kalman filter , computer science , global positioning system , estimator , navigation system , noise (video) , statistic , nonlinear system , control theory (sociology) , algorithm , artificial intelligence , mathematics , statistics , telecommunications , physics , control (management) , quantum mechanics , image (mathematics)

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

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