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Research on the UWB/IMU fusion positioning of mobile vehicle based on motion constraints

Research on the UWB/IMU fusion positioning of mobile vehicle based on motion constraints In a non-line-of-sight (NLOS) environment, high accuracy ultra-wideband (UWB) positioning has been one of the hot topics in studying indoor positioning. Aiming at the UWB and inertial measurement unit (IMU) fusion vehicle positioning, a constraint robust iterate extended Kalman filter (CRIEKF) algorithm has been proposed in this paper. It has overcome the innate defect of the extended Kalman filter against non-Gaussian noise and the shortcoming of the robust extended Kalman filter algorithm, which has just processed the non-Gaussian noise solely based on the prior information. Our algorithm can update the observation covariance based on the posteriori estimate of the system in each iteration, and then update the posteriori distribution of the system based on the obtained covariance to significantly reduce the influence of non-Gaussian noise on positioning accuracy. Also, with the introduction of motion constraints, such as zero velocity, pseudo velocity and plane constraints, it can achieve a smoother positioning result. The experimental result proves that through the CRIEKF-based UWB/IMU fusion robot positioning method, a mean positioning accuracy of around 0.21 m can be achieved in NLOS environments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Acta Geodaetica et Geophysica" Springer Journals

Research on the UWB/IMU fusion positioning of mobile vehicle based on motion constraints

"Acta Geodaetica et Geophysica" , Volume 55 (2) – Jun 3, 2020

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Publisher
Springer Journals
Copyright
Copyright © Akadémiai Kiadó 2020
ISSN
2213-5812
eISSN
2213-5820
DOI
10.1007/s40328-020-00291-8
Publisher site
See Article on Publisher Site

Abstract

In a non-line-of-sight (NLOS) environment, high accuracy ultra-wideband (UWB) positioning has been one of the hot topics in studying indoor positioning. Aiming at the UWB and inertial measurement unit (IMU) fusion vehicle positioning, a constraint robust iterate extended Kalman filter (CRIEKF) algorithm has been proposed in this paper. It has overcome the innate defect of the extended Kalman filter against non-Gaussian noise and the shortcoming of the robust extended Kalman filter algorithm, which has just processed the non-Gaussian noise solely based on the prior information. Our algorithm can update the observation covariance based on the posteriori estimate of the system in each iteration, and then update the posteriori distribution of the system based on the obtained covariance to significantly reduce the influence of non-Gaussian noise on positioning accuracy. Also, with the introduction of motion constraints, such as zero velocity, pseudo velocity and plane constraints, it can achieve a smoother positioning result. The experimental result proves that through the CRIEKF-based UWB/IMU fusion robot positioning method, a mean positioning accuracy of around 0.21 m can be achieved in NLOS environments.

Journal

"Acta Geodaetica et Geophysica"Springer Journals

Published: Jun 3, 2020

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