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

Author

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  • Jianhua Cheng
  • Daidai Chen
  • Rene Jr. Landry
  • Lin Zhao
  • Dongxue Guan

Abstract

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.

Suggested Citation

  • Jianhua Cheng & Daidai Chen & Rene Jr. Landry & Lin Zhao & Dongxue Guan, 2014. "An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-8, March.
    • Handle: RePEc:hin:jnljam:451939
    DOI: 10.1155/2014/451939
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