Application of the Ensemble Kalman Filter to GPS/inertial Integration

X. Wang, D.A. Grejner-Brzezinska, C.K. Toth

Abstract:	The integration of GPS (Global Positioning System) and IMU (Inertial Measurement Unit)/INS (Inertial Navigation System) technologies has become the navigation standard, and high-accuracy, continuous navigation solution can be obtained under good satellite geometry and atmospheric conditions. When fusing GPS and IMU measurements, a filtering technique is generally applied to optimally estimate the position, velocity, and orientation of the platform as well as the sensor errors and other nuisance parameters. A number of filters have been developed to integrate information from different sources. Among them, EKF (Extended Kalman Filter), UKF (Unscented Kalman Filter), PF (Particle Filter), and their variants, are frequently used in the GPS/INS navigation field. In addition to the above filters, another non-linear filter, Ensemble Kalman Filter (EnKF), has been applied in many applications in the field of meteorology and oceanography. Similarly to PF, EnKF is also samplebased nonlinear filter, based on the Sequential Monte Carlo (SMC) methods to represent the probability density of the state vector using a set of random samples associated with the corresponding weights. Despite the successes of EnKF in many applications, usage of EnKF in the navigation field is rarely found in the literature. This paper discusses the results of a preliminary study of the feasibility, benefits, and constraints of EnKF in GPS/IMU integration, with an objective to address the dilemma between positioning accuracy and computational efficiency. The preliminary test result with simulated data set shows that EnKF with EKF are comparable in a linear model, while more efforts are still needed to accomplish reliable EnKF filtering of an actual GPS/IMU data set.
Published in:	Proceedings of the 2011 International Technical Meeting of The Institute of Navigation January 24 - 26, 2011 Catamaran Resort Hotel San Diego, CA
Pages:	819 - 825
Cite this article:	Wang, X., Grejner-Brzezinska, D.A., Toth, C.K., "Application of the Ensemble Kalman Filter to GPS/inertial Integration," Proceedings of the 2011 International Technical Meeting of The Institute of Navigation, San Diego, CA, January 2011, pp. 819-825.
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