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Sensors 2018, 18(5), 1592; https://doi.org/10.3390/s18051592

Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error

1
Department of Mathematics, Physics and Computer Science, University of Parma, 43124 Parma, Italy
2
Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy
*
Author to whom correspondence should be addressed.
Received: 18 March 2018 / Revised: 11 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
(This article belongs to the Section Internet of Things)
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Abstract

Interest in the Internet of Things (IoT) is rapidly increasing, as the number of connected devices is exponentially growing. One of the application scenarios envisaged for IoT technologies involves indoor localization and context awareness. In this paper, we focus on a localization approach that relies on a particular type of communication technology, namely Ultra Wide Band (UWB). UWB technology is an attractive choice for indoor localization, owing to its high accuracy. Since localization algorithms typically rely on estimated inter-node distances, the goal of this paper is to evaluate the improvement brought by a simple (linear) statistical model of the distance error. On the basis of an extensive experimental measurement campaign, we propose a general analytical framework, based on a Least Square (LS) method, to derive a novel statistical model for the range estimation error between a pair of UWB nodes. The proposed statistical model is then applied to improve the performance of a few illustrative localization algorithms in various realistic scenarios. The obtained experimental results show that the use of the proposed statistical model improves the accuracy of the considered localization algorithms with a reduction of the localization error up to 66%. View Full-Text
Keywords: ultra wide band; experimental model; indoor localization; Internet of Things; least square method ultra wide band; experimental model; indoor localization; Internet of Things; least square method
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Monica, S.; Ferrari, G. Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error. Sensors 2018, 18, 1592.

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