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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 451939, 8 pages
http://dx.doi.org/10.1155/2014/451939
Research Article

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

1Marine Navigation Research Institute, College of Automation, Harbin Engineering University, Harbin 150001, China
2LASSENA Laboratoire, Ecole de Technologie Superieure, Université du Québec, Montréal, Canada H3C 1K3

Received 13 November 2013; Accepted 15 February 2014; Published 20 March 2014

Academic Editor: Yuxin Zhao

Copyright © 2014 Jianhua Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.