Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances

Alarifi, Abdulrahman; Al-Salman, AbdulMalik S.; Alsaleh, Mansour; Alnafessah, Ahmad; Al-Hadhrami, Suheer; Al-Ammar, Mai A.; Al‐Khalifa, Hend S.

doi:10.3390/s16050707

Cited by 864 publications

(671 citation statements)

References 81 publications

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“…Especially, Ultrawide Band (UWB), a low power RF signal with wide spectrum of frequency bands, can be used to measure distance by TDoA or Time of Flight (ToF) technique. Moreover, using UWB even enables user to determine Angle of Arrival (AoA) as reviewed in [25]. Ubisense 7000 sensor applied both of these techniques (TDoA and AoA).…”

Section: Introductionmentioning

confidence: 99%

Improvement of Ultrasound-based System Using Sine Wave Detector and CAN Network

Tan¹

2017

Preprint

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This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

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Section: Introductionmentioning

confidence: 99%

Improvement of Ultrasound-based System Using Sine Wave Detector and CAN Network

Tan¹

2017

Preprint

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“…However, this method also neglects the damages. Kim, Choi, Alarifi and Anumba [17][18][19][20] estimated the error when applying TDOA localization algorithm by using the Kalman filtering, which increased the positioning accuracy but complicated the computation. Tang and Park [21][22] implemented target localization by geometric method, but the localization region was limited.…”

Section: Introductionmentioning

confidence: 99%

TDOA Localization Algorithm based on Weight Fusion of Target Position Coordinates

QingYi¹,

Zhang²,

Sun³

2017

JESTR

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The use time of difference of arrival (TDOA) or angle of arrival (AOA) in locating the target requires at least four base stations. The measurement accuracy of different base stations varies due to different relative positions of the base station and the target. Therefore, the measurement results of different base stations cannot be discussed simultaneously. A new TDOA localization algorithm based on weighting fusion of position coordinates of target source was proposed to address this problem. First, maximum likelihood estimation was used to acquire the position coordinates of multiple groups of target sources on the 2D plane with time of arrival (TOA) and AOA of signals from base stations to the target source. Second, the coordinates were applied with weighted fusion according to the relative angle between the signals emitted from the base stations and target source to obtain relatively accurate 3D position coordinates. Finally, the proposed algorithm was compared with TDOA localization algorithm and TDOA-AOA combined localization algorithm. Results reveal that the proposed algorithm increases accuracy by 68.33% and 48.27% compared with TDOA localization algorithm and TDOA-AOA combined localization algorithm, respectively. Weighting data fusion can increase the calculation accuracy more effectively than the averaging method. This study increases the accuracy of 3D target localization in complicated environment. The proposed method shows prospect to optimize the wireless positioning technology in military and civilian fields.

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“…the deviation in the position fix grows with time As expressed in table 2, each location estimation algorithm has unique advantages and disadvantages, hence, using more than one type of positioning algorithms at the same time could get better performance depends on the indoor environment and application type. Hybrid methods seem promising as they are more tolerant to external side effect of interference and reflection (Alarifi et al, 2016). For instance, the combination of fingerprinting matching and trilateration technique enhances the accuracy of the user position in an indoor environment.…”

Section: Rss-basedmentioning

confidence: 99%

“…Due to this, the positioning system in RF based has a larger coverage area and needs less hardware comparing to other systems. (Farid et al, 2013) In comparison with outdoor environments, sensing location information within indoor environments requires a higher precision and is a more challenging task in part (Alarifi et al, 2016) because of severe multipath, low probability for availability of LoS path, and specific site parameters such as floor layout, moving objects, numerous reflecting surfaces, and disperse signals (Liu et al, 2007).…”

Section: Motivationmentioning

confidence: 99%

Rethinking Indoor Localization Solutions Towards the Future of Mobile Location-Based Services

Güney

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user's location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.

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Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances

Cited by 864 publications

References 81 publications

Improvement of Ultrasound-based System Using Sine Wave Detector and CAN Network

Improvement of Ultrasound-based System Using Sine Wave Detector and CAN Network

TDOA Localization Algorithm based on Weight Fusion of Target Position Coordinates

Rethinking Indoor Localization Solutions Towards the Future of Mobile Location-Based Services

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