Enhanced Kalman Filter for RISS/GPS Integrated Navigation using Gaussian Process Regression

M.M. Atia, A. Noureldin, M. Korenberg

Abstract:	The integration between Inertial Navigation Systems (INS) and Global Positioning System (GPS) based on Kalman Filter (KF) has been widely used. In INS/GPS systems, KF utilizes linearized dynamic models for INS errors modeling and GPS measurements. If Low-Cost MEMS-based inertial sensors with complex stochastic error nonlinearity are used, performance degrades significantly during short periods of GPS-outages due to the approximation introduced in the linearized INS errors dynamic model. This paper proposes a nonlinear data-driven INS-errors modeling based on Gaussian Process Regression (GPR). During reliable GPS availability, the correct vehicle state, sensors measurements, and INS output deviations from GPS measurements are collected. During GPS-outages, GPR is applied to recently collected data set to predict INS deviations from GPS. The predicted INS deviations are then fed to KF as a virtual update to estimate all INS errors. The proposed technique was tested with a low-cost Reduced Inertial Sensors System (RISS) for land-vehicles in which the vehicle odometer is used along with inertial sensors. Real road experiments on two different trajectories showed significant improvements during long GPS-outages.
Published in:	Proceedings of the 2012 International Technical Meeting of The Institute of Navigation January 30 - 1, 2012 Marriott Newport Beach Hotel & Spa Newport Beach, CA
Pages:	1148 - 1156
Cite this article:	Atia, M.M., Noureldin, A., Korenberg, M., "Enhanced Kalman Filter for RISS/GPS Integrated Navigation using Gaussian Process Regression," Proceedings of the 2012 International Technical Meeting of The Institute of Navigation, Newport Beach, CA, January 2012, pp. 1148-1156.
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