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Sensors in Indoor Positioning Systems

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

Deadline for manuscript submissions: closed (20 September 2021) | Viewed by 12461

Special Issue Editors

Department of Electronics, Polytechnic School Office O-217, University of Alcalá, Campus Universitario, 28871 Alcalá, Madrid, Spain
Interests: embedded systems; electronic design; intelligent sensors; HDL; industrial automation; architectures based on FPGAs; image and signal processing in embedded systems
Special Issues, Collections and Topics in MDPI journals
Department of Electronics, Polytechnic School Office O-322, University of Alcalá, Campus Universitario, 28871 Alcalá, Madrid, Spain
Interests: computer vision; parallel computing; image and IR sensors; motion planning and robot positioning; embedded electronic design; reconfigurable hardware
Special Issues, Collections and Topics in MDPI journals
Department of Electronics, Polytechnic School Office O-334, University of Alcalá, Campus Universitario, 28871 – Alcalá, Madrid, Spain
Interests: intelligent sensors; indoor positioning systems; visible light positioning; optical sensorial systems; sensor networks; electronic design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, there has been widespread technological evolution in the field of sensors. This has allowed for the development of numerous applications and services based on new sensors or conventional sensors whose typical applicability has been modified such as: to assist people, to locate users in large indoor environments in both professional and leisure activities, in logistic applications for intelligent factories, to perform different tasks such as moving objects within an environment, to assist in daily activities. It is a fact that, knowing the user position in indoor activities, provides new capacities to applications with significant added value.

Undoubtedly, the field of indoor environments is one of the major beneficiaries of this technological evolution. Thus, many contributions have been made in the field of the development of intelligent spaces that provide it with additional intelligence that makes life easier for humans. One of the most recursive applications has been the location of objects, robots, people, or even unusual situations based on the deployment of sensors for indoor environments.

This Special Issue is then an opportunity to disseminate among the scientific community relevant and new contributions related to

  • Development of sensors for indoor environments;
  • Indoor applications with sensors;
  • Application for mobile devices;
  • Intelligent methodologies for efficient sensor deployment;
  • New uses of sensors in indoor spaces;
  • New algorithm applied to sensors for intelligent environments;
  • Use of new and traditional technologies based on sensors for indoor spaces.

Prof. Dr. Ignacio Bravo-Muñoz
Dr. Alfredo Gardel-Vicente
Prof. Dr. José Luis Lázaro-Galilea
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.

Published Papers (5 papers)

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Research

19 pages, 980 KiB  
Article
Constant-Beamwidth Beamforming with Concentric Ring Arrays
by Avital Kleiman, Israel Cohen and Baruch Berdugo
Sensors 2021, 21(21), 7253; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217253 - 31 Oct 2021
Cited by 9 | Viewed by 2256
Abstract
Designing beampatterns with constant beamwidth over a wide range of frequencies is useful in many applications in speech, radar, sonar and communication. In this paper, we design constant-beamwidth beamformers for concentric ring arrays. The proposed beamformers utilize the circular geometry to provide improved [...] Read more.
Designing beampatterns with constant beamwidth over a wide range of frequencies is useful in many applications in speech, radar, sonar and communication. In this paper, we design constant-beamwidth beamformers for concentric ring arrays. The proposed beamformers utilize the circular geometry to provide improved beamwidth consistency compared to beamformers which are designed for linear sensor arrays of the same order. In the proposed configuration, all sensors on each ring share the same weight value. This constraint significantly simplifies the beamformers and reduces the hardware and computational resources required in a physical setup. Furthermore, a theoretical justification of the beamforming method is provided. We demonstrate the advantages of the proposed beamformers compared to the one-dimensional configuration in terms of directivity index, white noise gain and sidelobe attenuation. Full article
(This article belongs to the Special Issue Sensors in Indoor Positioning Systems)
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20 pages, 14288 KiB  
Article
Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery
by Jesús Sánchez-Pastor, Udaya S. K. P. Miriya Thanthrige, Furkan Ilgac, Alejandro Jiménez-Sáez, Peter Jung, Aydin Sezgin and Rolf Jakoby
Sensors 2021, 21(20), 6842; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206842 - 14 Oct 2021
Cited by 2 | Viewed by 2387
Abstract
Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag [...] Read more.
Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks. Full article
(This article belongs to the Special Issue Sensors in Indoor Positioning Systems)
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17 pages, 1546 KiB  
Article
Weak Calibration of a Visible Light Positioning System Based on a Position-Sensitive Detector: Positioning Error Assessment
by Álvaro De-La-Llana-Calvo, José-Luis Lázaro-Galilea, Alfredo Gardel-Vicente, David Salido-Monzú, Ignacio Bravo-Muñoz, Andreea Iamnitchi and Rubén Gil-Vera
Sensors 2021, 21(11), 3924; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113924 - 07 Jun 2021
Cited by 11 | Viewed by 2571
Abstract
Reduced deployment and calibration requirements are key for scalable and cost-effective indoor positioning systems. In this work, we propose a low-complexity, weak calibration procedure for an indoor positioning system based on infrastructure lighting and a positioning-sensitive detector. The proposed calibration relies on genetic [...] Read more.
Reduced deployment and calibration requirements are key for scalable and cost-effective indoor positioning systems. In this work, we propose a low-complexity, weak calibration procedure for an indoor positioning system based on infrastructure lighting and a positioning-sensitive detector. The proposed calibration relies on genetic algorithms to obtain the relevant system parameters in the real positioning environment without a priori information, and requires a low number of simple measurements. The achievable performance of the proposal was assessed by direct comparison with a formal offline calibration method requiring complex dedicated infrastructure and instruments. The comparative error assessment showed that the maximum accuracy reduction compared to the significantly more costly formal calibration was below 25 mm, and the overall absolute positioning error was smaller than 35 mm with orientation errors of around 0.25°. The performance achieved with the proposed weak calibration procedure is sufficient for many indoor positioning applications and largely reduces the cost and complexity of setting up the positioning system in real environments. Full article
(This article belongs to the Special Issue Sensors in Indoor Positioning Systems)
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19 pages, 984 KiB  
Article
Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment
by Chen Cheng, Ji Chang, Wenjun Lv, Yuping Wu, Kun Li, Zerui Li, Chenhui Yuan and Saifei Ma
Sensors 2020, 20(22), 6550; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226550 - 16 Nov 2020
Cited by 2 | Viewed by 1577
Abstract
The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate [...] Read more.
The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment. Full article
(This article belongs to the Special Issue Sensors in Indoor Positioning Systems)
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22 pages, 5001 KiB  
Article
Assessing the Feasibility of Augmenting Fall Detection Systems by Relying on UWB-Based Position Tracking and a Home Robot
by Maurizio Capra, Stefano Sapienza, Paolo Motto Ros, Alessio Serrani, Maurizio Martina, Alessandro Puiatti, Paolo Bonato and Danilo Demarchi
Sensors 2020, 20(18), 5361; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185361 - 18 Sep 2020
Cited by 6 | Viewed by 2753
Abstract
Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of [...] Read more.
Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand. Full article
(This article belongs to the Special Issue Sensors in Indoor Positioning Systems)
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