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Open AccessArticle

Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

Cognitronics and Sensor Systems Group, CITEC, Bielefeld University, 33619 Bielefeld, Germany
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Appl. Sci. 2020, 10(11), 3980; https://doi.org/10.3390/app10113980
Received: 2 April 2020 / Revised: 2 June 2020 / Accepted: 2 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Recent Advances in Indoor Localization Systems and Technologies)
In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn. View Full-Text
Keywords: UWB; NLOS identification; multi-path detection; NLOS and MP discrimination; machine learning; SVM; random forest; multilayer perceptron; LOS; DWM1000; indoor localization UWB; NLOS identification; multi-path detection; NLOS and MP discrimination; machine learning; SVM; random forest; multilayer perceptron; LOS; DWM1000; indoor localization
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    Doi: 10.4119/unibi/2943719
    Link: https://pub.uni-bielefeld.de/record/2943719
    Description: This experimental research data-set was used to present the multi-label classification results of UWB ranging system in our journal article entitled “Identification of NLOS and Multi-path Conditions in UWB Localization using Machine Learning Methods”. The research data includes the extracted features of UWB experimental data including their respective labels and the corresponding source code for the python machine learning library scikit-learn. The article was published in the special issue entitled “Recent Advances in Indoor Localization Systems and Technologies” at computing and artificial intelligence section, applied sciences journal, MDPI.
MDPI and ACS Style

Sang, C.L.; Steinhagen, B.; Homburg, J.D.; Adams, M.; Hesse, M.; Rückert, U. Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods. Appl. Sci. 2020, 10, 3980.

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