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Enhanced Kalman Filter using Noisy Input Gaussian Process Regression for Bridging GPS Outages in a POS

Published online by Cambridge University Press:  28 November 2017

Wen Ye ,
Zhanchao Liu Open the ORCID record for Zhanchao Liu [Opens in a new window] ,
Chi Li  and
Jiancheng Fang
Wen Ye*
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China)
Zhanchao Liu
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China)
Chi Li
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China)
Jiancheng Fang
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China)
*
(E-mail: wenye@buaa.edu.cn)
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Abstract

A Position and Orientation System (POS) integrating an Inertial Navigation Systems (INS) and the Global Positioning System (GPS) is a key component of remote sensing motion compensation. It can provide reliable and high-frequency high-precision motion information using a Kalman Filter (KF) during GPS availability. However, the performance of a POS significantly degrades during GPS outages. To maintain reliable POS outputs, this paper proposes a new hybrid predictor based on modelling the nonlinear time-series data-driven INS-errors using Noisy Input Gaussian Process Regression (NIGPR), which takes the input noise into account. The proposed approach is used to learn the nonlinear INS-errors model when GPS signals are available. When GPS outages occur, it starts to predict the observation measurement, and then feeds it to a KF as a virtual update to estimate all the INS errors. The proposed approach is verified in a real airplane, which combines a POS and Synthetic Aperture Radar (SAR). Experimental results show that the proposed approach significantly improves the performance of the POS, with improvements more than 90% better than a KF and 10% better than a Gaussian Process Regression (GPR/KF) combination during various GPS outages.

Keywords

Position and Orientation SystemKalman FilterGPS outagesNoisy Input Gaussian Process RegressionHybrid predictorTime series

Information

Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 565 - 584
Copyright
Copyright © The Royal Institute of Navigation 2017 

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