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Multi-Sensor Fusion for Indoor Localization

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 2799

Special Issue Editors


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Guest Editor
Department of Computer Science and Telecommunications; National Polytechnic Institute of Toulouse, University of Toulouse, 31071 Toulouse, France
Interests: computer networks and pervasive computing; IoT; smart environments; indoor localization

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Guest Editor
Inria Lille-Nord Europe, 59650 Villeneuve-d'Ascq, France
Interests: Internet of Things; wireless sensor networks; RFID; wireless robots networks; services (localization, neighbor discovery, etc)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first iPhone was released over a decade ago, triggering the smart-device revolution and placing powerful communication, computing, and sensing devices in our hands. Leveraging the multi-dimensional capabilities of smart devices has paved the way for a new generation of highly personalized and context-aware services. A fundamental building block, indoor localization has emerged as a major scientific and technological challenge. The solution space has grown richer over time, to include a variety of underlying sensor technologies – RF, IMU, acoustic, etc. – as mobile computing devices have evolved to include smartphones, tablets, RFID tags, wearables, and even micro-implants.

This Special Issue will bring together the latest research that leverages the multitude of sensors to address indoor localization challenges across the universe of smart devices and objects. This includes automatic indoor mapping, positioning, navigation, and tracking. There will be an emphasis on articles that introducing novel ideas and algorithms coupled with clever engineering using real-life prototype implementations and evaluations.

Topics of interests include, but are not limited to, the following:

  • Sensor fusion algorithms and implementations
  • Applications of machine learning to indoor localization
  • Localization and Internet of Things
  • Emerging localization and sensor-based applications
  • Emerging sensors for indoor localization
  • 3D localization
  • Localization of battery-free devices
  • In-body localization

Prof. Dr. Gentian Jakllari
Dr. Nathalie Mitton
Guest Editors

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • Indoor localization
  • radio localization
  • sensor fusion
  • dead reckoning
  • IMU sensors
  • acoustic sensors
  • motion tracking
  • Internet of Things
  • machine learning

Published Papers (1 paper)

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Research

27 pages, 2980 KiB  
Article
Novel Solutions to the Three-Anchor ToA-Based Three-Dimensional Positioning Problem
by Mohamed Khalaf-Allah
Sensors 2021, 21(21), 7325; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217325 - 03 Nov 2021
Cited by 6 | Viewed by 2065
Abstract
At least four non-coplanar anchor nodes (ANs) are required for the time-of-arrival (ToA)-based three-dimensional (3D) positioning to enable unique position estimation. Direct method (DM) and particle filter (PF) algorithms were developed to address the three-anchor ToA-based 3D positioning problem. The proposed DM reduces [...] Read more.
At least four non-coplanar anchor nodes (ANs) are required for the time-of-arrival (ToA)-based three-dimensional (3D) positioning to enable unique position estimation. Direct method (DM) and particle filter (PF) algorithms were developed to address the three-anchor ToA-based 3D positioning problem. The proposed DM reduces this problem to the solution of a quadratic equation, exploiting the knowledge about the workspace, to first estimate the x- or z-coordinate, and then the remaining two coordinates. The implemented PF uses 1000 particles to represent the posterior probability density function (PDF) of the AN’s 3D position. The prediction step generates new particles by a resampling procedure. The ToA measurements determine the importance of these particles to enable updating the posterior PDF and estimating the 3D position of the AN. Simulation results corroborate the viability of the developed DM and PF algorithms, in terms of accuracy and computational cost, in the pursuit and circumnavigation scenarios, and even with a horizontally coplanar arrangement of the three ANs. Therefore, it is possible to enable applications requiring real-time positioning, such as unmanned aerial vehicle (UAV) autonomous docking and circling a stationary (or moving) position, without the need for an excessive number of ANs. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion for Indoor Localization)
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