Special Issue "Wireless Localization: Tracking and Navigation Data Set"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 3815

Special Issue Editors

Prof. Dr. Aleš Prokeš
E-Mail Website
Guest Editor
Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 3082/12, 616 00 Brno, Czech Republic
Interests: RF channel measurement and modeling; V2X communication, localization, and positioning; optimization of receivers for free-space optical systems; influence of atmospheric channel on optical signal propagation; higher order non-uniform sampling and signal reconstruction; software-defined radio
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Special Issue Information

Dear Colleagues,

In the past few decades, wireless localization tracking and navigation techniques have made considerable progress. Their growing importance can be observed in many areas of everyday life, including, for example, precise and reliable navigation in traffic leading to the integration of autonomous vehicles in a future intelligent transportation system, localization and tracking of people and goods for surveillance purposes, control of farming machines in agriculture to maximize harvest, or control of self-driving vehicles in manufacturing plants and warehouses to automate the flow of materials. The future localization and navigation systems will have to be able to operate reliably in most diverse conditions, including indoor tracking scenarios without support from global satellite navigation. Such systems will require the fusion of data sets gathered by multiple positioning systems (based on WiFi, UWB, LTE or other networks) and motion sensors in combination with techniques, such as computer vision or artificial intelligence in order to facilitate seamless and robust navigation.

This Special Issue is aimed at sharing new ideas and R&D results with researchers, academics, and experts working in the area of localization techniques, algorithms and strategies, including network-based positioning systems and inertial sensor data fusion, as well as prospective signal processing techniques leading to increase the reliability, accuracy, and robustness of both outdoor and indoor object localization, navigation and tracking. The topics expected include, but are not limited to the following areas:

  • Advanced algorithms and signal processing techniques
  • Performance analysis and optimization
  • Hybrid system data fusion, cooperative techniques
  • Innovative design of devices, signals, and systems
  • Location-based services and applications

Prof. Aleš Prokeš
Dr. Joaquín Torres-Sospedra
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning
Data 2019, 4(1), 12; https://doi.org/10.3390/data4010012 - 12 Jan 2019
Cited by 24 | Viewed by 3549
RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in [...] Read more.
RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at −12 dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the −4 dBm, −12 dBm and −20 dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license. Full article
(This article belongs to the Special Issue Wireless Localization: Tracking and Navigation Data Set)
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