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Multisensors Indoor Localization
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Multisensors Indoor Localization

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

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 4910

Special Issue Editors

Prof. Dr. Marcin Grzegorzek

E-Mail Website
Guest Editor
Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
Interests: pattern recognition; medical informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Frank Deinzer

E-Mail Website
Guest Editor
University of Applied Sciences,Fachhochschule Würzburg-Schweinfurtdisabled, Wurzburg, Germany
Interests: Machine Learning; Pattern Recognition; Computer Graphics; Sensor Fusion; Indoor Localization

Special Issue Information

We have all become very accustomed to location-based services making our lives easier. Navigation systems in cars and destination guidance systems for pedestrians in city centers have become part of everyday life. However, people’s daily life also consists of activities that take place indoor: at home, at work, at leisure places like shopping malls and museums and at unknown places during holidays or business trips. In these indoor scenarios, technologies that work well outdoors fail only because the signals of the global navigation satellite systems are too weak to be received inside buildings. This makes it difficult to offer context-aware systems where location is a key feature for software functionality related to social networking, advertising, recommendation systems, or healthcare.

Indoor localization systems offer solutions for the technical aspect of this location estimation problem. The systems differ in many ways: which information sources in terms of sensors, knowledge, hardware, and infrastructure are available? What do the models and algorithms look like? Are different information sources combined? All these aspects influence the current research in this area. One important finding of research in recent years is that information from multiple sensors is needed and must be fused in one way or another.

This Special Issue is dedicated to the goal of offering outstanding papers in the described field of multisensors indoor localization.

Prof. Dr. Marcin Grzegorzek
Prof. Dr. Frank Deinzer
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
  • Sensors
  • Multi-Sensor Fusion
  • Pedestrian Navigation System
  • Pedestrian Dead Reckoning
  • Models

Published Papers (2 papers)

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Research

10 pages, 457 KiB  
Article
Using Barometer for Floor Assignation within Statistical Indoor Localization
by Toni Fetzer, Frank Ebner, Frank Deinzer and Marcin Grzegorzek
Sensors 2023, 23(1), 80; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010080 - 22 Dec 2022
Cited by 2 | Viewed by 1585
Abstract
This paper presents methods for floor assignation within an indoor localization system. We integrate the barometer of the phone as an additional sensor to detect floor changes. In contrast to state-of-the-art methods, our statistical model uses a discrete state variable as floor information, [...] Read more.
This paper presents methods for floor assignation within an indoor localization system. We integrate the barometer of the phone as an additional sensor to detect floor changes. In contrast to state-of-the-art methods, our statistical model uses a discrete state variable as floor information, instead of a continuous one. Due to the inconsistency of the barometric sensor data, our approach is based on relative pressure readings. All we need beforehand is the ceiling height including the ceiling’s thickness. Further, we discuss several variations of our method depending on the deployment scenario. Since a barometer alone is not able to detect the position of a pedestrian, we additionally incorporate Wi-Fi, iBeacons, Step and Turn Detection statistically in our experiments. This enables a realistic evaluation of our methods for floor assignation. The experimental results show that the usage of a barometer within 3D indoor localization systems can be highly recommended. In nearly all test cases, our approach improves the positioning accuracy while also keeping the update rates low. Full article
(This article belongs to the Special Issue Multisensors Indoor Localization)
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24 pages, 2915 KiB  
Article
Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks
by Xijia Wei, Zhiqiang Wei and Valentin Radu
Sensors 2021, 21(22), 7488; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227488 - 11 Nov 2021
Cited by 6 | Viewed by 2417
Abstract
Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localization system, [...] Read more.
Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localization system, MM-Loc, relying on zero hand-engineered features, but learning automatically from data instead. This is achieved by using modality-specific neural networks to extract preliminary features from each sensing modality, which are then combined by cross-modality neural structures. We show that our choice of modality-specific neural architectures can estimate the location independently. But for better accuracy, a multimodal neural network that fuses the features of early modality-specific representations is a better proposition. Our proposed MM-Loc system is tested on cross-modality samples characterised by different sampling rate and data representation (inertial sensors, magnetic and WiFi signals), outperforming traditional approaches for location estimation. MM-Loc elegantly trains directly from data unlike conventional indoor positioning systems, which rely on human intuition. Full article
(This article belongs to the Special Issue Multisensors Indoor Localization)
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