The Merits of UKF and PF for Integrated INS/GPS Navigation Systems

S. Saeedi, N. El-Sheimy, Z. Syed

Abstract:	Current implementations of integrated navigation systems are commonly done using Extended Kalman Filter (EKF) to improve low-cost Micro Electro Mechanical Systems (MEMS) sensors by Global Positioning System (GPS) observations. The EKF assumes linear process in measurement models and the noise to be Gaussian, which is unrealistic for highly nonlinear systems. To overcome these limitations, the use of Unscented Kalman Filter (UKF) and Particle Filter (PF) has recently been proposed in MEMS/GPS integrated system. UKF deals with nonlinearity using the unscented transformation (UT) of the generated sigma points but maintain the assumption of the noise to be Gaussian. PF is a non-parametric filter and hence can easily deal with non-linearity and non-Gaussian noises. In this paper, we made an attempt to evaluate different filtering techniques for integrating GPS/INS navigation systems to achieve better navigation accuracy for MEMS sensors integrated by GPS data.
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:	811 - 817
Cite this article:	Saeedi, S., El-Sheimy, N., Syed, Z., "The Merits of UKF and PF for Integrated INS/GPS Navigation Systems," Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009), Savannah, GA, September 2009, pp. 811-817.
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