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Open AccessArticle

A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging

by 1, 1, 2,*, 1 and 1
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China
Australian Centre for Field Robotics, The University of Sydney, Sydney, NSW 2006, Australia
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 440;
Received: 22 November 2018 / Revised: 19 December 2018 / Accepted: 19 January 2019 / Published: 22 January 2019
(This article belongs to the Section Sensor Networks)
Ultra-wideband (UWB) sensors have been widely used in multi-robot systems for cooperative tracking and positioning purposes due to their advantages such as high ranging accuracy and good real-time performance. In order to reduce the influence of non-line-of-sight (NLOS) UWB communication caused by the presence of obstacles on ranging accuracy in indoor environments, the paper proposes a novel Bayesian filtering approach for UWB ranging error mitigation. Nonparametric UWB sensor models, namely received signal strength (RSS) model and time of arrival (TOA) model, are constructed to capture the probabilistic noise characteristics under the influence of different obstruction conditions and materials within a typical indoor environment. The proposed Bayesian filtering approach can be used either as a standalone error mitigation approach for peer-to-peer (P2P) ranging, or as a part of a higher level Bayesian state estimation framework. Experiments were conducted to validate and evaluate the proposed approach in two configurations, i.e., inter-robot ranging, and mobile robot tracking in a wireless sensor network. The experimental results show that the proposed method can accurately identify the line-of-sight (LOS) and NLOS scenarios with wood and metal obstacles in a probabilistic representation and effectively improve the ranging/tracking accuracy. In addition, the low computational overhead of the approach makes it attractive in real-time systems. View Full-Text
Keywords: UWB ranging; NLOS; error mitigation; probabilistic sensor model; Bayesian filtering UWB ranging; NLOS; error mitigation; probabilistic sensor model; Bayesian filtering
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MDPI and ACS Style

Xin, J.; Gao, K.; Shan, M.; Yan, B.; Liu, D. A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging. Sensors 2019, 19, 440.

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