An Intelligent Real-Time MEMS IMU/HSGPS Integrated Vehicular Navigation System and Road Test Results

J-H. Wang, Y. Gao

Abstract:	The challenges to low-cost Micro-Electro-Mechanical System (MEMS) INS and high sensitivity GPS (HSGPS) integration arise from dealing with the corrupted HSGPS data in signal-degraded environments and the large instrument errors experienced with low-grade MEMS sensors. This research develops intelligent data fusion and processing techniques for such a low-cost integration system by incorporating the Artificial Intelligence (AI) with the Kalman filtering. Two cascaded Kalman filters implemented upon a loosely coupled integration scheme are applied to perform data fusion in the velocity/attitude and position domain, respectively. A fuzzy GPS data classification system is developed to optimize INS/GPS data fusion through adjusting the measurement noise covariances of the Kalman filters according to GPS signal degradation conditions. A dynamics knowledge aided inertial navigation algorithm is created to reduce and control INS error drift through simplifying system models and extending measurement update schemes of the Kalman filters. The intelligent integration algorithm has been implemented into real-time integration software and tested in urban environments. The test results have demonstrated the capability of using the intelligent realtime MEMS IMU/HSGPS integrated system for continuous and reliable vehicular navigation.
Published in:	Proceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2006) September 26 - 29, 2006 Fort Worth Convention Center Fort Worth, TX
Pages:	2165 - 2173
Cite this article:	Wang, J-H., Gao, Y., "An Intelligent Real-Time MEMS IMU/HSGPS Integrated Vehicular Navigation System and Road Test Results," Proceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2006), Fort Worth, TX, September 2006, pp. 2165-2173.
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