Hybrid Extended Particle Filter (HEPF) for INS/GPS Integrated System

Priyanka Aggarwal

Abstract:	Navigation comprises the integration of methodologies and systems for estimating the time varying position, velocity and attitude of moving objects. Integration of INS/GPS systems for improved navigation requires extensive evaluations of nonlinear equations involving double integration. Currently, integrated navigation systems are commonly implemented using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The EKF assumes linear process and measurement models while UKF generates sigma points using the real mean and standard deviation of data. However, both EKF and UKF assume the noise to be Gaussian, which is unrealistic for highly nonlinear systems. To overcome these limitations, Particle Filter (PF) was proposed lately which is a non-parametric filter and hence can easily deal with non-linearity and non- Gaussian noises. In this paper, Hybrid Extended Particle Filter (HEPF) is developed as an alternative to the EKF to achieve better navigation data accuracy using low cost MEMS sensors. Experimental GPS/INS datasets using carrier phase GPS receiver data and inertial measurements from low cost MEMS-grade Inertial Measurement Unit (IMU) is used to evaluate the proposed HEPF. The HEPF performance is compared to that of other estimation techniques such as the EKF. The results show that both HEPF and EKF provide comparable navigation results during periods without GPS outages. However in cases when 60 seconds GPS outages are simulated, HEPF performs much better than the EKF, especially when simulated outages are located in periods with high vehicle dynamics.
Published in:	Proceedings of the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2008) September 16 - 19, 2008 Savannah International Convention Center Savannah, GA
Pages:	1600 - 1609
Cite this article:	Aggarwal, Priyanka, "Hybrid Extended Particle Filter (HEPF) for INS/GPS Integrated System," Proceedings of the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2008), Savannah, GA, September 2008, pp. 1600-1609.
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