NLOS Detection and Mitigation in Differential Localization Topologies Based on UWB Devices

Zandian, Reza; Witkowski, Ulf

doi:10.1109/ipin.2018.8533781

Cited by 13 publications

(7 citation statements)

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“…This motivated us to classify the UWB ranging systems into three classes (LOS, NLOS, MP) to improve the location accuracy in the UWB localization system. The classified ranging information is applicable in any positioning algorithm [6,14] to mitigate the biases effectively [2,18,20] caused by the NLOS and MP conditions. It should be noted that the measured distances in Figure 2d were conducted in the static scenario at approximately a 6 m distance between the anchor and tag for the three classes.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The common approach is to detect the non-direct path signal (i.e., NLOS and/or MP) and use the detected information to modify the location algorithm in order to mitigate the biases caused by the NLOS and/or MP conditions [9,10,17,18,20,21,30]. In this manuscript, we divided the related works into two subsections: (i) conventional approaches without using ML techniques (Section 3.1) and (ii) ML-based approaches (Section 3.2).…”

Section: Related Workmentioning

confidence: 99%

“…Identification and mitigation techniques of the NLOS condition in UWB, or wireless communications in general, using ML methods are not new. It has received significant interest recently years [9][10][11][17][18][19][20][21][22][23]. However, the major contributions in the literature address the binary classification between the LOS and NLOS in the UWB ranging system.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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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Section: Problem Descriptionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…This motivates us the classification of the UWB ranging systems into three classes (LOS, NLOS, MP) to improve the location accuracy in UWB localization system. The classified ranging information is applicable in any positioning algorithm [6,12] to effectively mitigate the biases [2,16,18] caused by the NLOS and MP conditions.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Identification and mitigation techniques of NLOS condition in UWB, or wireless communications in general, using ML methods are not new. It has been received significant interests in recent years [9][10][11][15][16][17][18][19][20][21]. However, the major contributions in the literature address the binary classification between the LOS and NLOS in UWB ranging system.…”

Section: Introductionmentioning

confidence: 99%

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

Sang¹,

Steinhagen²,

Homburg³

et al. 2020

Preprint

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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 are 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 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. Though, 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 mentioned three classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental data-set. The data-set was collected in different conditions at different scenarios in indoor environments. Using the collected real measurement data, we compare 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 show that applying ML methods in UWB ranging systems are effective in identification of the above-mentioned three classes. In specific, the overall accuracy reaches up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it is 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we (will) 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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Body Centric Localization and Tracking Using Compact Wearable Antennas

Koul

Bharadwaj

2021

Lecture Notes in Electrical Engineering

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NLOS Detection and Mitigation in Differential Localization Topologies Based on UWB Devices

Cited by 13 publications

References 31 publications

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

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

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

Body Centric Localization and Tracking Using Compact Wearable Antennas

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