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Recent Advances in Indoor Positioning Systems
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Recent Advances in Indoor Positioning Systems

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

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 11413

Special Issue Editor

Dr. Antonio Moschitta

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: short-range positioning; statistical signal processing; battery characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor positioning systems (IPSs) are subject to significant research activities, since they provide position information where global navigation satellite systems are unavailable or operate with a reduced accuracy. IPSs can enable several IoT-related applications, belonging to fields such as domotics, ambient assisted living (AAL), and location-based Services. The targeted accuracy may range from a few meters to a few centimeters or less, depending on the application. Additional requirements can involve real-time operations and simultaneous tracking of multiple items.

The position is often estimated by transmitting given signals with a known propagation model between the mobile node to be located and a set of known position anchors, measuring specific signal parameters at the receiver side. Additional information can be obtained using image analysis and passive sensors, such as inertial measurement units or infrared sensors. Measurement data are then processed by various positioning algorithms that make use of statistical signal processing, fingerprinting, sensor fusion, and, more recently, machine learning techniques.

Several tradeoffs do apply, relating accuracy, measurement rate, system complexity, power consumption, and lifetime of battery powered units. It is worth noting that small and low-cost platforms with significant processing capabilities are available, such as systems-on-a-chip and smartphones, easily interfaced to multiple sensors and capable of running complex algorithms.

This Special Issue targets novel research results for IPSs or short-range positioning, focused mostly, but not exclusively, on sensor characteristics, system architecture and operation, design tradeoffs, positioning algorithms, and overall positioning and tracking performance.

Prof. Antonio Moschitta
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. 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

  • localization
  • tracking
  • indoor positioning
  • sensors
  • sensor networks
  • signal processing
  • data fusion
  • machine learning
  • accuracy

Published Papers (4 papers)

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Research

21 pages, 4177 KiB  
Article
Simulation-Based Resilience Quantification of an Indoor Ultrasound Localization System in the Presence of Disruptions
by Aishvarya Kumar Jain, Dominik Jan Schott, Hermann Scheithauer, Ivo Häring, Fabian Höflinger, Georg Fischer, Emanuël A. P. Habets, Patrick Gelhausen, Christian Schindelhauer and Stefan Johann Rupitsch
Sensors 2021, 21(19), 6332; https://doi.org/10.3390/s21196332 - 22 Sep 2021
Cited by 3 | Viewed by 1854
Abstract
Time difference of arrival (TDOA) based indoor ultrasound localization systems are prone to multiple disruptions and demand reliable, and resilient position accuracy during operation. In this challenging context, a missing link to evaluate the performance of such systems is a simulation approach to [...] Read more.
Time difference of arrival (TDOA) based indoor ultrasound localization systems are prone to multiple disruptions and demand reliable, and resilient position accuracy during operation. In this challenging context, a missing link to evaluate the performance of such systems is a simulation approach to test their robustness in the presence of disruptions. This approach cannot only replace experiments in early phases of development but could also be used to study susceptibility, robustness, response, and recovery in case of disruptions. The paper presents a simulation framework for a TDOA-based indoor ultrasound localization system and ways to introduce different types of disruptions. This framework can be used to test the performance of TDOA-based localization algorithms in the presence of disruptions. Resilience quantification results are presented for representative disruptions. Based on these quantities, it is found that localization with arc-tangent cost function is approximately 30% more resilient than the linear cost function. The simulation approach is shown to apply to resilience engineering and can be used to increase the efficiency and quality of indoor localization methods. Full article
(This article belongs to the Special Issue Recent Advances in Indoor Positioning Systems)
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23 pages, 3943 KiB  
Article
Cooperative Networked PIR Detection System for Indoor Human Localization
by Chia-Ming Wu, Xuan-Ying Chen, Chih-Yu Wen and William A. Sethares
Sensors 2021, 21(18), 6180; https://doi.org/10.3390/s21186180 - 15 Sep 2021
Cited by 10 | Viewed by 2749
Abstract
Pyroelectric Infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems can be categorized as wearable and non-wearable systems, where the latter are also known as device-free localization systems. Since the binary [...] Read more.
Pyroelectric Infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems can be categorized as wearable and non-wearable systems, where the latter are also known as device-free localization systems. Since the binary PIR sensor detects only the presence of a human motion in its field of view (FOV) without any other information about the actual location, utilizing the information of overlapping FOV of multiple sensors can be useful for localization. In this study, a PIR detector and sensing signal processing algorithms were designed based on the characteristics of the PIR sensor. We applied the designed PIR detector as a sensor node to create a non-wearable cooperative indoor human localization system. To improve the system performance, signal processing algorithms and refinement schemes (i.e., the Kalman filter, a Transferable Belief Model, and a TBM-based hybrid approach (TBM + Kalman filter)) were applied and compared. Experimental results indicated system stability and improved positioning accuracy, thus providing an indoor cooperative localization framework for PIR sensor networks. Full article
(This article belongs to the Special Issue Recent Advances in Indoor Positioning Systems)
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15 pages, 3678 KiB  
Article
Parkinson’s Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements
by Filippo Milano, Gianni Cerro, Francesco Santoni, Alessio De Angelis, Gianfranco Miele, Angelo Rodio, Antonio Moschitta, Luigi Ferrigno and Paolo Carbone
Sensors 2021, 21(12), 4196; https://doi.org/10.3390/s21124196 - 18 Jun 2021
Cited by 13 | Viewed by 3233
Abstract
Reliable diagnosis of early-stage Parkinson’s disease is an important task, since it permits the administration of a timely treatment, slowing the progression of the disease. Together with non-motor symptoms, other important signs of disease can be retrieved from the measurement of the movement [...] Read more.
Reliable diagnosis of early-stage Parkinson’s disease is an important task, since it permits the administration of a timely treatment, slowing the progression of the disease. Together with non-motor symptoms, other important signs of disease can be retrieved from the measurement of the movement trajectory and from tremor appearances. To measure these signs, the paper proposes a magnetic tracking system able to collect information about translational and vibrational movements in a spatial cubic domain, using a low-cost, low-power and highly accurate solution. These features allow the usage of the proposed technology to realize a portable monitoring system, that may be operated at home or in general practices, enabling telemedicine and preventing saturation of large neurological centers. Validation is based on three tests: movement trajectory tracking, a rest tremor test and a finger tapping test. These tests are considered in the Unified Parkinson’s Disease Rating Scale and are provided as case studies to prove the system’s capabilities to track and detect tremor frequencies. In the case of the tapping test, a preliminary classification scheme is also proposed to discriminate between healthy and ill patients. No human patients are involved in the tests, and most cases are emulated by means of a robotic arm, suitably driven to perform required tasks. Tapping test results show a classification accuracy of about 93% using a k-NN classification algorithm, while imposed tremor frequencies have been correctly detected by the system in the other two tests. Full article
(This article belongs to the Special Issue Recent Advances in Indoor Positioning Systems)
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23 pages, 3756 KiB  
Article
DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization
by Marius Laska and Jörg Blankenbach
Sensors 2021, 21(6), 2000; https://doi.org/10.3390/s21062000 - 12 Mar 2021
Cited by 18 | Viewed by 2355
Abstract
Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. [...] Read more.
Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain. Full article
(This article belongs to the Special Issue Recent Advances in Indoor Positioning Systems)
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