Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
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 sy...
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| Format: | Article |
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MDPI AG
2020-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/11/3980 |
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| author | Cung Lian Sang Bastian Steinhagen Jonas Dominik Homburg Michael Adams Marc Hesse Ulrich Rückert |
| author_facet | Cung Lian Sang Bastian Steinhagen Jonas Dominik Homburg Michael Adams Marc Hesse Ulrich Rückert |
| author_sort | Cung Lian Sang |
| collection | DOAJ |
| description | 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. |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:18:07Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9bdcf8fe72814260bf34aeedd970e2d12023-11-20T03:12:24ZengMDPI AGApplied Sciences2076-34172020-06-011011398010.3390/app10113980Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning MethodsCung Lian Sang0Bastian Steinhagen1Jonas Dominik Homburg2Michael Adams3Marc Hesse4Ulrich Rückert5Cognitronics and Sensor Systems Group, CITEC, Bielefeld University, 33619 Bielefeld, GermanyCognitronics and Sensor Systems Group, CITEC, Bielefeld University, 33619 Bielefeld, GermanyCognitronics and Sensor Systems Group, CITEC, Bielefeld University, 33619 Bielefeld, GermanyCognitronics and Sensor Systems Group, CITEC, Bielefeld University, 33619 Bielefeld, GermanyCognitronics and Sensor Systems Group, CITEC, Bielefeld University, 33619 Bielefeld, GermanyCognitronics and Sensor Systems Group, CITEC, Bielefeld University, 33619 Bielefeld, GermanyIn 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.https://www.mdpi.com/2076-3417/10/11/3980UWBNLOS identificationmulti-path detectionNLOS and MP discriminationmachine learningSVM |
| spellingShingle | Cung Lian Sang Bastian Steinhagen Jonas Dominik Homburg Michael Adams Marc Hesse Ulrich Rückert Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods Applied Sciences UWB NLOS identification multi-path detection NLOS and MP discrimination machine learning SVM |
| title | Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods |
| title_full | Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods |
| title_fullStr | Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods |
| title_full_unstemmed | Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods |
| title_short | Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods |
| title_sort | identification of nlos and multi path conditions in uwb localization using machine learning methods |
| topic | UWB NLOS identification multi-path detection NLOS and MP discrimination machine learning SVM |
| url | https://www.mdpi.com/2076-3417/10/11/3980 |
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