Accuracy Assessment and Learned Error Mitigation of UWB ToF Ranging

Schmid, Lorenz; Salido-Monzú, David; Wieser, Andreas

doi:10.1109/ipin.2019.8911769

Cited by 36 publications

(33 citation statements)

References 17 publications

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“…The research data of the mentioned work are publicly available in [12]. Similar results based on UWB were also reported in [13]. Therefore, differentiation between the LOS, NLOS, and MP conditions in wireless ranging systems is important for precise localization systems.…”

Section: Introductionmentioning

confidence: 59%

“…There have been several proposals in the literature [9,16,27,28,[31][32][33] to identify and mitigate the NLOS conditions in UWB. However, the identification process of the MP condition is usually ignored in the literature, although the effects of the MP conditions in UWB ranging systems were acknowledged as important aspects in [3,8,13,21]. Conventionally, the NLOS detection in UWB is always regarded as a binary classification problem.…”

Section: Conventional Nlos Identification Techniques In Uwbmentioning

confidence: 99%

“…Moreover, the unsupervised machine learning technique called "expectation maximization for Gaussian mixture models" was recently applied in [39] to classify the LOS and NLOS conditions in UWB measurement. Likewise, deep learning approaches such as the convolutional neural network (CNN) were also explored to distinguish the NLOS condition from LOS in UWB ranging [13,22]. In CNN-based deep learning approaches, the authors generally modified the existing CNN network such as GoogLeNet [22], VGG-architecture (i.e., VGG-16 or VGG-19) [13,22], AlexNet [22], etc., to be usable for the low cost UWB systems.…”

Section: Identification Of the Nlos And Mp Conditions In The Literatumentioning

confidence: 99%

“…Likewise, deep learning approaches such as the convolutional neural network (CNN) were also explored to distinguish the NLOS condition from LOS in UWB ranging [13,22]. In CNN-based deep learning approaches, the authors generally modified the existing CNN network such as GoogLeNet [22], VGG-architecture (i.e., VGG-16 or VGG-19) [13,22], AlexNet [22], etc., to be usable for the low cost UWB systems. The reported overall accuracy ranges started from 60% (using a typical ML technique such as SVM) up to 99% (using the CNN approach).…”

Section: Identification Of the Nlos And Mp Conditions In The Literatumentioning

confidence: 99%

“…In contrast to the binary classification between LOS and NLOS in UWB, the binary classification of the MP from LOS conditions was investigated in [13]. The author reported that MP effects could cause an error in UWB ranging from a few centimeters up to 60 cm.…”

Section: Identification Of the Nlos And Mp Conditions In The Literatumentioning

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: Introductionmentioning

confidence: 59%

Section: Conventional Nlos Identification Techniques In Uwbmentioning

confidence: 99%

Section: Identification Of the Nlos And Mp Conditions In The Literatumentioning

confidence: 99%

Section: Identification Of the Nlos And Mp Conditions In The Literatumentioning

confidence: 99%

Section: Identification Of the Nlos And Mp Conditions In The Literatumentioning

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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Ultra-Low-Power Range Error Mitigation for Ultra-Wideband Precise Localization

Angarano

Salvetti

Mazzia

et al. 2022

Lecture Notes in Networks and Systems

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Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications. Ultra-wideband (UWB) localization technology represents a valuable low-cost solution to the problem. However, non-line-of-sight (NLOS) conditions and complexity of the specific radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efficient methodology.

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