An integrated IMU and UWB sensor based indoor positioning system

Yao, Leehter; Wu, Yuh‐Yih; Yao, Lei; Liao, Zhe Zheng

doi:10.1109/ipin.2017.8115911

Cited by 87 publications

(47 citation statements)

References 8 publications

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“…However, the fusion algorithm does not use the original measurement data of UWB, but uses the estimated three-dimensional coordinates (x, y, z) of the UWB system as the observation of the Kalman filter. In order to overcome the shortcomings of UWB and IMU, Yao et al [18] merged the two by extending the Kalman filter (EKF). The UWB was used to calibrate or estimate the IMU error, while the IMU was used to detect and isolate the damaged UWB sensor data to improve overall positioning performance.…”

Section: Related Workmentioning

confidence: 99%

UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter

Zeng

Liu

2019

Sensors

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Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy.

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Section: Related Workmentioning

confidence: 99%

UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter

Zeng

Liu

2019

Sensors

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“…One approach is the position based detection scheme, where the source of trigger is our existing infrastructure—the movement of AT devices can be estimated based on the real-time location data-streams. The other approach includes the usage of external trigger sources such as separate modules or attached sensors—for example, Inertial Measurement Unit (IMU) [ 57 ]. These units provide information about an object’s special force (acceleration) and angular rate, so it can be applied as a supplementary unit for tracking [ 58 ], but this technique is based on sensor fusion which is computation heavy process.…”

Section: Overall System Architecturementioning

confidence: 99%

Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0

Frankó

Vida

Varga

2020

Sensors

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Revolutionizing logistics and supply chain management in smart manufacturing is one of the main goals of the Industry 4.0 movement. Emerging technologies such as autonomous vehicles, Cyber-Physical Systems and digital twins enable highly automated and optimized solutions in these fields to achieve full traceability of individual products. Tracking various assets within shop-floors and the warehouse is a focal point of asset management; its aim is to enhance the efficiency of logistical tasks. Global players implement their own solutions based on the state of the art technologies. Small and medium companies, however, are still skeptic toward identification based tracking methods, because of the lack of low-cost and reliable solutions. This paper presents a novel, working, reliable, low-cost, scalable solution for asset tracking, supporting global asset management for Industry4.0. The solution uses high accuracy indoor positioning—based on Ultra-Wideband (UWB) radio technology—combined with RFID-based tracking features. Identifying assets is one of the most challenging parts of this work, so this paper focuses on how different identification approaches can be combined to facilitate an efficient and reliable identification scheme.

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“…In order to address the shortcomings of some individual technology, many researchers implement filters to fuse UWB and IMU (Liu et al, 2017) (Yao et al, 2017) to improve precision. The main process is to adjust the parameter in the filtering based on the performance of two data resources so that the corrected position can be presented.…”

Section: Algorithm Derivationmentioning

confidence: 99%

Review on Ultra Wide Band Indoor Localization

Chi

Liu

et al. 2020

IJoCED

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Ultra-Wide Band (UWB) is an important means of indoor positioning. Carrying out the research of UWB is of great significance to the development of indoor positioning technology. This article gives a review of the application of UWB in indoor positioning. The motivation and development status of UWB are introduced. UWB localization algorithms such as received signal strength indication, time of arrival, time difference of arrival are analyzed one by one. In this paper, the derivations of the algorithm are summarized. Several technical difficulties of UWB technology development and future development of UWB are presented. This paper provides researchers with a clear insight into the UWB indoor positioning system so that they can further develop other advanced techniques.

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An integrated IMU and UWB sensor based indoor positioning system

Cited by 87 publications

References 8 publications

UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter

UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter

Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0

Review on Ultra Wide Band Indoor Localization

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