An Analytical Study of Time of Flight Error Estimation in Two-Way Ranging Methods

Sang, Cung Lian; Adams, Michael; Hörmann, Timm; Hesse, Marc; Porrmann, Mario; Rückert, Ulrich

doi:10.1109/ipin.2018.8533697

Cited by 27 publications

(28 citation statements)

References 16 publications

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“…To minimize the mentioned inaccuracy and typical sources of error, several two‐way ranging () methods are available in the literature . The existing solutions include the single‐sided TWR (SS‐TWR), the symmetric double‐sided TWR (SDS‐TWR), and asymmetric double‐sided TWR (ADS‐TWR).…”

Section: Uwb Ranging Errors and Channel Quality Indicatorsmentioning

confidence: 99%

“…However, in the NLOS and multipath propagation condition, the ToF measurement error is also affected by external error sources ( e NLOS ). These external error sources can be categorized as follows Propagation time delay (PTD).…”

Section: Uwb Ranging Errors and Channel Quality Indicatorsmentioning

confidence: 99%

“…To minimize the mentioned inaccuracy and typical sources of error, several two-way ranging () methods are available in the literature. [17][18][19][20][21][22] The existing solutions include the single-sided TWR (SS-TWR), the symmetric double-sided TWR (SDS-TWR), and asymmetric double-sided TWR (ADS-TWR). For example, in SDS-TWR protocol, each of the UWB nodes has an estimate of the round trip time T r and turnaround time T ta as depicted in Equation (1):…”

Section: The Sources Of Tof Measurement Errormentioning

confidence: 99%

“…These external error sources can be categorized as follows. 22 • Propagation time delay (PTD). PTD occurs in two cases: (a) when the direct signal path is obstructed/blocked completely and (b) when the signal has to transverse through different materials.…”

Section: The Sources Of Tof Measurement Errormentioning

confidence: 99%

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A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

Musa

Nugraha

Han

et al. 2019

Int J Communication

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Summary Among existing wireless technologies, ultra‐wideband (UWB) is the most promising solution for indoor location tracking. UWB has a great multipath fading immunity; however, great multipath resolvability alone does not eliminate the effect of non‐line‐of‐sight (NLOS) and multipath propagation. NLOS and multipath propagation in indoor environments can easily produce meters of UWB ranging error. This condition gives an enormous impact on the accuracy of indoor location tracking data. To address this problem, we propose an NLOS detection method using recursive decision tree learning. Using the UWB channel quality indicators information, we develop our model with the Gini index and altered priors splitting criteria. We then validate the constructed model using the 10‐fold cross‐validation method. Our experiment shows that the constructed model has correctly detected 90% of both line‐of‐sight (LOS) and NLOS cases on the seven different indoor environments. The result of this work can be used for the UWB indoor location tracking accuracy improvement.

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Section: Uwb Ranging Errors and Channel Quality Indicatorsmentioning

confidence: 99%

Section: Uwb Ranging Errors and Channel Quality Indicatorsmentioning

confidence: 99%

Section: The Sources Of Tof Measurement Errormentioning

confidence: 99%

Section: The Sources Of Tof Measurement Errormentioning

confidence: 99%

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A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

Musa

Nugraha

Han

et al. 2019

Int J Communication

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“…In wireless ranging systems including UWB technology, the distance between the transmitter and receiver is estimated by measuring the time-of-flight (TOF) between the two transceivers and multiplying it by the speed of light [7,8]. However, the ranging algorithm assumes that the TOFsignal is always in a direct line-of-sight (LOS) condition.…”

Section: Introductionmentioning

confidence: 99%

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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Two Way Ranging Positioning Methods Analysis Under Optimized Sensor Deployments

Ferrero-Guillén,

Díez-González,

Álvarez

et al. 2024

Lecture Notes in Networks and Systems

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An Analytical Study of Time of Flight Error Estimation in Two-Way Ranging Methods

Cited by 27 publications

References 16 publications

A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

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

Two Way Ranging Positioning Methods Analysis Under Optimized Sensor Deployments

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