Ultra-Wideband Positioning System Using TWR and Lateration Methods

Duru, Anday; Sehirli, Eftal; Kabalcı, İdris

doi:10.1145/3234698.3234756

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Because each anchor is at a different distance from the tag, the tag transmits a signal and arrives at all anchors at different times. The difference in the time the signal arrives at the two anchors can be used to calculate hyperbolic [40], [41], [42], [43].…”

Section: ) Owr-based Multilateration Positioning Methodsmentioning

confidence: 99%

Accurate Indoor Positioning for UWB-Based Personal Devices Using Deep Learning

2023

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Recently, there has been a rapidly emerging demand for localization technologies to provide various location-based services in indoor environments, such as smart buildings, smart factories, and parking lots, as well as outdoor environments. Ultra-wideband (UWB), an emerging wireless technology, provides opportunities for precise indoor positioning with sub-meter accuracy, much more accurate than WiFi or BLE-based techniques, thanks to its signal and communication characteristics. UWB technology has recently begun to be applied to personal devices such as smartphones and is expected to be used for indoor localization of personal devices. However, personal devices often cause signal problems because they are worn on human hands or bodies and move dynamically or are in a non-line-of-sight (NLoS) condition, such as pockets or bags. Therefore, these challenges in the dynamic environment of personal devices must be addressed to enable accurate indoor positioning services based on UWB. In this paper, we propose a novel UWB-based indoor positioning approach that significantly improves localization accuracy under dynamic personal device environments. Our proposed approach detects various NLoS conditions of personal environments by leveraging channel impulse response (CIR) and deep learning. Based on the detected NLoS conditions, the proposed approach adjusts the Kalman filter to adaptively estimate the position of the target UWB-based personal device. Specifically, the distance measurement errors by NLoS are minimized by applying weights determined by deep learning to the Kalman filter. Through experiments conducted in practical indoor environments, we have shown that our proposed approach considerably improves the accuracy compared to the traditional Kalman filter-based and trilateration approaches. According to our method of counting the number of points relative to ground truth, the proposed positioning system improved positioning accuracy significantly by 20.84% to 27.22% with an error tolerance of ±25 cm and by 7.78% to 22.78% with an error tolerance of ±50 cm, compared to the traditional approaches.

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Section: ) Owr-based Multilateration Positioning Methodsmentioning

confidence: 99%

Accurate Indoor Positioning for UWB-Based Personal Devices Using Deep Learning

2023

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“…The distance between two devices is calculated by using the time a message signal travels from device A to device B and back to A [7]. As soon as at least three distances to different receivers are available, the tag position can be determined by using the trilateration method [8].…”

Section: Measuring Methods and Characteristics Of The Localino Uwb Sy...mentioning

confidence: 99%

Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems

Neuber

Schmitt

Engelmann

et al. 2022

Sensors

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In recent years, the use of indoor localization techniques has increased significantly in a large number of areas, including industry and healthcare, primarily for monitoring and tracking reasons. From the field of radio frequency technologies, an ultra-wideband (UWB) system offers comparatively high accuracy and is therefore suitable for use cases with high precision requirements in position determination, for example for localizing an employee when interacting with a machine tool on the shopfloor. Indoor positioning systems with radio signals are influenced by environmental obstacles. Although the influence of building structures like walls and furniture was already analysed in the literature before, the influence of metal machine tools was not yet evaluated concerning the accuracy of the position determination. Accordingly, the research question for this article is defined: To what extent is the positioning accuracy of the UWB system influenced by a metal machine tool?The accuracy was measured in a test setup, which consists of a total of four scenarios in a production environment. For this purpose, the visual contact between the transmitter and the receiver modules, including the influence of further interfering factors of a commercially available indoor positioning system, was improved step by step from scenario 1 to 4. A laser tracker was used as the reference measuring device. The data was analysed based on the type A evaluation of standard uncertainty according to the guide to the expression of uncertainty in measurement (GUM). It was possible to show an improvement in standard deviation from 87.64cm±32.27cm to 6.07cm±2.24cm with confidence level 95% and thus provides conclusions about the setup of an indoor positioning system on the shopfloor.

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“…The two-way ranging (TWR) method [12][13][14] was developed to calculate the distance between two nodes by measuring the turnaround time of a pulse transmitted between the two nodes in which an Anchor node (Node 1) sends the pulse to a Tag node (Node 2) that transmits the pulse back to Node 1, and then by subtracting the processing time at Node 2, as shown in Figure 2.…”

Section: Uwb Positioning Algorithmmentioning

confidence: 99%

UWB and MEMS IMU Integrated Positioning Algorithm for a Work-Tool Tracking System

Kwon

et al. 2021

Applied Sciences

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In this paper, we address a system that can accurately locate and monitor work tools in a complex assembly process, such as automotive production. Our positioning monitoring system is positioned by a combined sensor of the UWB module and the MEMS IMU (inertial measuring unit) sensor based on the extended Kalman filter. The MEMS IMU sensor provides the positioning calibration information. The proposed method incorporates IMU and UWB positioning to compensate for errors that can only occur in UWB positioning through the extended Kalman filter (EKT). This EKT is improved by the error dynamic equation derived from the sparse state-space matrix. Also, the proposed method computes the transmission time and distance between the tag and anchor of the UWB module by the TWR (two-way range) system. The tag of a mobile node, which is attached to a moving tool, measures the position of the work tool and transmits the position coordinate data to the anchor. Here, the proposed method uses the trilateration localization method by the confidence distance compensation to prevent the distance error by obstacles and changes in the indoor environment. Experimental results verified that the proposed method confirms whether a specific tool is accurately used according to the prescribed regulations and has more positioning accuracy than the conventional methods.

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Ultra-Wideband Positioning System Using TWR and Lateration Methods

Cited by 10 publications

References 5 publications

Accurate Indoor Positioning for UWB-Based Personal Devices Using Deep Learning

Accurate Indoor Positioning for UWB-Based Personal Devices Using Deep Learning

Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems

UWB and MEMS IMU Integrated Positioning Algorithm for a Work-Tool Tracking System

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