Efficient AoA-Based Wireless Indoor Localization for Hospital Outpatients Using Mobile Devices

Hou, Yanbin; Yang, Xiaodong; Abbasi, Qammer H.

doi:10.3390/s18113698

Cited by 69 publications

(43 citation statements)

References 47 publications

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“…This use is based on several communication technologies and calculation methods of AoA. In [3] and [4] [9] or Subspace-Based as MUSIC (Multiple Signal Classification) [11].…”

Section: State Of the Artmentioning

confidence: 99%

Performances Analysis of a System of Localization by Angle of Arrival UWB Radio

Diagne¹,

Val²,

Farota³

et al. 2020

IJCNS

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The increasingly widespread use of sensor and actuator networks and in general of the Internet of Things (IoT) in several areas of precision, imposes upon localization systems that can often equip them with a robust and more precise localization. It is in this sense that UWB technology has proved to be one of the most powerful communication technologies for these localization systems; thanks, in particular to the bandwidth occupied instantaneously by the signal allowing a very fine temporal resolution. Constructors have set up localization kits based on various technologies. These kits facilitate in a way the work of localization of users. In this paper, we present results on the performance study of the Decawave PDoA Kit. This Kit uses the PDoA (Phase Difference of Arrival) to determine the Angle of Arrival (AoA) parameter with UWB technology. This study is in context of localization by AoA for an application to protect agricultural crops against grain-eating birds. The results of the study show overall AoA measurement errors around 10 degrees in an ideal environment.

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“…This use is based on several communication technologies and calculation methods of AoA. In [3] and [4] [9] or Subspace-Based as MUSIC (Multiple Signal Classification) [11].…”

Section: State Of the Artmentioning

confidence: 99%

Performances Analysis of a System of Localization by Angle of Arrival UWB Radio

Diagne¹,

Val²,

Farota³

et al. 2020

IJCNS

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“…In this paper, we understand indoor localisation to be an epitome of technologies and implementations for localisation in an enclosed environment. Examples of few such environments range from, but are not limited to, residential abodes [13], commercial shopping malls [160], industrial halls and factories [73], hospitals [64] and natural formations, such as underwater caves [99]. Here we consider sensor combinations stemming from the necessities imposed by these environments.…”

Section: Semantic Understanding Of Indoor Positioning and Trackingmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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“…The distance measurement refers to calculating the distance or orientation between the unknown target node and the anchor node connected thereto through communication between them [12]. The classical distance measurement algorithms include based on signal time of arrival (TOA), signal time difference of arrival (TDOA) [13], Angle of Arrival (AOA) [14] and received signal strength (RSSI) [15,16] algorithms.…”

Section: The Measurement Of Distance Between the Target Node And The mentioning

confidence: 99%

“…The distance between the anchor node and the target node can be recalculated based on the median Cdev as shown in equation (14).…”

Section: The Calculation Of the Distance Deviation Coefficientmentioning

confidence: 99%

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Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN

Chen¹,

Wang²,

Ouyang³

et al. 2019

Preprint

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The node position information is critical in the wireless sensor network (WSN). However, the existing positioning algorithms commonly have low positioning accuracy because of noise interferences in communication. To solve this problem, this paper presents an iterative positioning model based on distance correction to improve the positioning accuracy of the target node in WSN. First, the log-distance distribution model of received signal strength indication (RSSI) ranging is built and the noise impact factor is derived based on the model. Second, the initial position coordinates of the target node are obtained based on the triangle centroid localization algorithm, thereby calculating the distance deviation coefficient under the influence of noise. Then, the ratio of the distance measured by the log-normal distribution model to the median distance deviation coefficient is taken as the new distance between the anchor node and the target node. Based on the new distance, the triangular centroid positioning algorithm is used again to calculate the target node coordinates. Finally, the iterative positioning model is constructed, and the distance deviation coefficient is updated repeatedly to update the positioning result until the set number of iterations is reached. Experiment results show that the proposed iterative positioning model can improve positioning accuracy effectively.

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Efficient AoA-Based Wireless Indoor Localization for Hospital Outpatients Using Mobile Devices

Cited by 69 publications

References 47 publications

Performances Analysis of a System of Localization by Angle of Arrival UWB Radio

Performances Analysis of a System of Localization by Angle of Arrival UWB Radio

Sensor Modalities and Fusion for Robust Indoor Localisation

Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN

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