A Multimodal Fingerprint-Based Indoor Positioning System for Airports

Molina, Beatriz Lucas; Olivares, Eneko; Palau, Carlos E.; Esteve, Manuel

doi:10.1109/access.2018.2798918

Cited by 97 publications

(58 citation statements)

References 38 publications

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“…In this communication, the problem of the indoor location, which is based on the signals available in the wireless devices that comprise Wi-Fi and Wi-Max networks within the broadband wireless systems, is presented [7][8][9][10]. The location process employs a fingerprinting technique that operates with the relationship between the levels of power and/or relative delays between signals due to the multipath by reflections effect [11]. By comparing other techniques, such as the angle of arrival (AOA) or time of arrival (TOA), which presents several limitations due to multipath effects and non-line of sight (N-LOS), implementation of the fingerprinting technique is relatively easy [12].…”

Section: Introductionmentioning

confidence: 99%

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

et al. 2019

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The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.

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Section: Introductionmentioning

confidence: 99%

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

et al. 2019

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“…With the increased popularity of the IoT, people in their daily lives are surrounded by more and smarter devices such as laptops, smartphones, and tablets that are capable of collecting RSSI signals [25], [26]. BLE beacons are small size, low-cost devices that can be used for indoor localization [27], [28]. IoT devices can be found in a plethora of application for smart cities [29], [30], smart homes [31], healthcare [32] and many more [33]- [35].…”

Section: Related Workmentioning

confidence: 99%

BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum

Spachos

Plataniotis

2020

IEEE Systems Journal

166

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The Internet of Things (IoT) can enable smart infrastructures to provide advanced services to the users. New technological advancement can improve our everyday life, even simple tasks as a visit to the museum. In this paper, an indoor localization system is presented, to enhance the user experience in a museum. In particular, the proposed system relies on Bluetooth Low Energy (BLE) beacons proximity and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. At the same time, an RSS-based technique is used to estimate the location of the visitor in the museum. An Android application is developed to estimate the distance from the exhibits and collect useful analytics regarding each visit and provide a recommendation to the users. Moreover, the application implements a simple Kalman filter in the smartphone, without the need of the Cloud, to improve localization precision and accuracy. Experimental results on distance estimation, location, and detection accuracy show that BLE beacon is a promising solution for an interactive smart museum. The proposed system has been designed to be easily extensible to the IoT technologies and its effectiveness has been evaluated through experimentation.

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“…The accuracy of centroid positioning relies heavily on the density and distribution of anchor nodes [13][14][15]. The positioning is very accurate when the nodes are dense and uniformly distributed.…”

Section: Initial Positioning Based On Weighted Centroid Algorithmmentioning

confidence: 99%

A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

Ren¹,

Huang²,

Song³

et al. 2019

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Considering the high accuracy needed for indoor positioning, this paper develops a novel indoor positioning algorithm for the wireless sensor network (WSN) in the following steps. First, the RSSIs of the network nodes were sampled and analyzed, and the excess errors were filtered to enhance positioning accuracy. Next, the initial position was iteratively obtained by the weighted centroid algorithm, and a correction matrix was developed to improve the Taylor series expansion (TSE), and the final position was determined through improved TSE iteration. The proposed positioning method was verified through simulation.

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A Multimodal Fingerprint-Based Indoor Positioning System for Airports

Cited by 97 publications

References 38 publications

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum

A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

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