Special Issue "Advanced Sensors and Sensing Technologies for Indoor Localization"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics".

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 6000

Special Issue Editors

Prof. Dr. Alessio De Angelis
E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: indoor positioning; magnetic positioning; instrumentation and measurement; statistical signal processing; UWB systems
Dr. Francesco Santoni
E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: indoor positioning; microelectronics; semiconductors; particle physics

Special Issue Information

Accurate information about the position of users, devices, mobile robots, or systems is crucial for the safe and efficient operation of applications in the industrial, commercial, and consumer fields. High-accuracy short-range localization is important also for specialized applications such as hand and finger tracking for biomedical or telemanipulation scenarios.

Particular research interest is devoted to those environments where the availability and accuracy of global navigation satellite systems is challenging, such as indoors. In such environments, positioning may be achieved by a wide array of sensor technologies and processing methods.

This Special Issue aims to gather contributions on sensors for localization, which may include, but are not limited to, radio-frequency, magnetic-field, imaging, inertial, acoustic, and ultrasound sensors. Furthermore, the scope of this Special Issue covers also sensing and processing methods applied to the positioning problem. These include robust numerical methods, optimization strategies, tracking algorithms, machine learning, and performance characterization and validation methods.

Prof. Dr. Alessio De Angelis
Dr. Francesco Santoni
Guest Editors

Manuscript Submission Information

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Keywords

  • Indoor positioning
  • Radio-frequency localization
  • Magnetic-field sensors
  • Ultrasound positioning sensors and systems
  • Tracking
  • Machine learning for positioning

Published Papers (7 papers)

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Editorial

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Editorial
Advanced Sensors and Sensing Technologies for Indoor Localization
Appl. Sci. 2022, 12(8), 3786; https://doi.org/10.3390/app12083786 - 08 Apr 2022
Viewed by 365
Abstract
Accurate information concerning the position of users, devices, mobile robots, or systems is crucial for the safe and efficient operation of applications in the industrial, commercial, and consumer fields [...] Full article
(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies for Indoor Localization)

Research

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Article
Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
Appl. Sci. 2021, 11(22), 10607; https://doi.org/10.3390/app112210607 - 11 Nov 2021
Cited by 6 | Viewed by 921
Abstract
This article addresses the problem of determining the location of pallets carried by forklifts inside a warehouse, which are recognized thanks to an onboard Radio Frequency IDentification (RFID) system at the ultra-high-frequency (UHF) band. By reconstructing the forklift trajectory and orientation, the location [...] Read more.
This article addresses the problem of determining the location of pallets carried by forklifts inside a warehouse, which are recognized thanks to an onboard Radio Frequency IDentification (RFID) system at the ultra-high-frequency (UHF) band. By reconstructing the forklift trajectory and orientation, the location of the pallets can be associated with the forklift position at the time of unloading events. The localization task is accomplished by means of an easy-to-deploy combination of onboard sensors, i.e., an inertial measurement unit (IMU) and an optical flow sensor (OFS), with a commercial ultra-wideband (UWB) system through an Unscented Kalman Filter (UKF) algorithm, which estimates the forklift pose over time. The proposed sensor fusion approach contributes to the localization error mitigation by preventing drifts in the trajectory reconstruction. The designed methos was at first evaluated by means of a simulation framework and then through an experimental analysis conducted in a large warehouse with a size of about 4000 m2. Full article
(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies for Indoor Localization)
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Article
Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning
Appl. Sci. 2021, 11(15), 6805; https://doi.org/10.3390/app11156805 - 24 Jul 2021
Cited by 7 | Viewed by 732
Abstract
Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones [...] Read more.
Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs. Full article
(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies for Indoor Localization)
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Article
Ble Based Indoor Positioning System and Minimal Zone Searching Algorithm (MZS) Applied to Visitor Trajectories within a Museum
Appl. Sci. 2021, 11(13), 6107; https://doi.org/10.3390/app11136107 - 30 Jun 2021
Cited by 1 | Viewed by 705
Abstract
Museums are perfect experimentation grounds for indoor positioning technologies. Indeed, museum managers are always pleased to hold these kinds of events where it offers the opportunity to the public to be a part of such experimentation and allowing us at the same time [...] Read more.
Museums are perfect experimentation grounds for indoor positioning technologies. Indeed, museum managers are always pleased to hold these kinds of events where it offers the opportunity to the public to be a part of such experimentation and allowing us at the same time to popularize our research with them. In this paper, we describe an experiment that held within the museum of natural history of La Rochelle with a class of high school volunteers. We will explain our systems that has been built to work in this specific case, and among other things formalize our algorithm for indoor localization that has not had an equivalent in the state of the art. The minimal zone searching algorithm (MZS) can compute in real time the position of a visitor, shaped as a zone with an average surface of 3 m2 when resources are limited and when the placement of nodes must respect the constraints imposed by the room’s layout. This method offered good results with data collected during the experimentation, such as a meaningful representation of the position of a visitor and most importantly a stable execution during the whole experience even when the subject was in tight spaces. Full article
(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies for Indoor Localization)
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Article
Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System
Appl. Sci. 2021, 11(12), 5594; https://doi.org/10.3390/app11125594 - 17 Jun 2021
Cited by 5 | Viewed by 1002
Abstract
Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, [...] Read more.
Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 × 30 × 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven. Full article
(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies for Indoor Localization)
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Article
Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
Appl. Sci. 2021, 11(9), 4049; https://doi.org/10.3390/app11094049 - 29 Apr 2021
Cited by 4 | Viewed by 606
Abstract
In this paper, we propose an RFID-based location-positioning platform for managing cart operations at vast and fast-moving distribution facilities. Our work was motivated by a real-world problem in a large airmail center. Our system requires that each cart is affixed with an active [...] Read more.
In this paper, we propose an RFID-based location-positioning platform for managing cart operations at vast and fast-moving distribution facilities. Our work was motivated by a real-world problem in a large airmail center. Our system requires that each cart is affixed with an active RFID tag, and RFID readers are installed at multiple locations of the facility. The locations of the tagged carts are determined by the estimated angles of arrival communication signals between the readers and the tags and the signal strengths, and the information about their positions are stored in the platform continuously. This platform enables the users to locate tagged objects in real time, thereby saving staff members a significant amount of time from searching for the right carts in the highly dynamic environment. Consequently, the mail facility can benefit from the increased efficiency. Furthermore, the system is also able to reduce the chance of having a delay in delivering mail bags to unit load device (ULD) area and misplacements of mail bags to ULDs. We further analyze the the huge amount of historical data collected from the RFID infrastructure of the airmail center for the cart movements within the facility and observed previously unrecognized operational issues. We also discuss some of the challenges and problems faced in this project. We believe that this application can bring huge benefits to businesses and organizations along supply chains by effectively anticipating the increasing demand for logistics services in the age of electronic retailing. Full article
(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies for Indoor Localization)
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Review

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Review
An Introduction to Indoor Localization Techniques. Case of Study: A Multi-Trilateration-Based Localization System with User–Environment Interaction Feature
Appl. Sci. 2021, 11(16), 7392; https://doi.org/10.3390/app11167392 - 11 Aug 2021
Cited by 3 | Viewed by 700
Abstract
The problem of estimating the indoor position of a person or an object, also known as indoor localization, has gained a lot of interest in the last decades. Actually, this feature would be valuable in many application contexts, from logistics to robotic and [...] Read more.
The problem of estimating the indoor position of a person or an object, also known as indoor localization, has gained a lot of interest in the last decades. Actually, this feature would be valuable in many application contexts, from logistics to robotic and Assistive Technology. Different solutions have been proposed in the literature, exploiting a wide range of approaches. This paper aims to provide a brief review of the state-of-the-art approaches in the field, as well as to present the RESIMA case study. The latter exploits an ultrasound-based indoor localization system and a User–Environment Interaction functionality, which allows for performing the continuous estimation of the distance between the end-user and objects in the environment. The latter is valuable to provide the end-user with efficient assistance during the environment exploitation. The main focus of this work is related to the overall description of the system architecture, the trilateration algorithm adopted for the sake of user localization and the estimation of the delay time produced by user-distance computation under different operating conditions. Full article
(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies for Indoor Localization)
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