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Sensors 2017, 17(5), 1159; doi:10.3390/s17051159

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach

1
Xsens Technologies B.V., Enschede 7521 PR, The Netherlands
2
Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Cheng Wang, Julian Smit, Ayman F. Habib and Michael Ying Yang
Received: 31 March 2017 / Revised: 8 May 2017 / Accepted: 10 May 2017 / Published: 19 May 2017
(This article belongs to the Special Issue Multi-Sensor Integration and Fusion)
View Full-Text   |   Download PDF [1695 KB, uploaded 19 May 2017]   |  

Abstract

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem. View Full-Text
Keywords: multi-sensor fusion; state estimation; moving horizon estimation; nonlinear optimization; inertial navigation multi-sensor fusion; state estimation; moving horizon estimation; nonlinear optimization; inertial navigation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Girrbach, F.; Hol, J.D.; Bellusci, G.; Diehl, M. Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach. Sensors 2017, 17, 1159.

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