Nonlinear Modeling and Identification of Inertial Errors with Application to 2D Vehicle Navigation

Z. Shen, J. Georgy, M. Korenberg, A. Noureldin

Abstract:	This paper introduces an improved approach to low-cost 2D vehicular navigation by integrating a reduced inertial sensor system (RISS) with a global positioning system (GPS). Due to their low cost, small size, light weight, and low power consumption, Micro-Electro-Mechanical- System (MEMS) based inertial sensors are preferred for vehicular navigation and they are used in this research. Despite the advantages of MEMS-based inertial sensors, they suffer from severe error characteristics that are stochastic in nature. This leads to serious positional error growth if they work unaided. To enhance the overall positioning accuracy especially during GPS outages, Fast Orthogonal Search (FOS) is employed and cascaded to Kalman Filter (KF) based RISS/GPS integration. By modeling and reducing both linear and nonlinear system errors, the augmented KF/FOS method provides much better navigation accuracy than the KF-only algorithm. The proposed technique is examined by real road tests on a land vehicle. The results demonstrate the superior performance of the proposed KF/FOS method over the KF-only solution.
Published in:	Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009) September 22 - 25, 2009 Savannah International Convention Center Savannah, GA
Pages:	593 - 599
Cite this article:	Shen, Z., Georgy, J., Korenberg, M., Noureldin, A., "Nonlinear Modeling and Identification of Inertial Errors with Application to 2D Vehicle Navigation," Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009), Savannah, GA, September 2009, pp. 593-599.
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