Special Issue "Indoor Positioning Techniques"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 30 October 2020.

Special Issue Editors

Prof. Dr. Antonio Moschitta
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Guest Editor
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: ADC; DAC; TDC; ADC testing; Indoor positioning; magnetic positioning systems; UWB positioning systems; Ultrasound positioning systems; Power Quality; Statistical signal Processing; Quantile Based Estimator; Kalman Filter; distributed measurement systems
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Prof. Dr. Jörg Blankenbach
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Guest Editor
Geodetic Institute, RWTH Aachen University, 52074 Aachen, Germany
Interests: indoor positioning; distributed GI systems; digital photogrammetry & laser scanning; building information modeling (BIM)
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Prof. Dr. Domenico Capriglione
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Guest Editor
Department of Industrial Engineering, University of Salerno, 84084 Fisciano (SA), Italy
Interests: Distributed Measurement Systems with self-diagnostic capability; Testing methods for measurement software characterization; metrological characterization of image-based measurement systems; Measurement for the electromagnetic compatibility; Measurements on telecomunication and internet based networks.
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor positioning techniques (IPTs) are a strong enabler for various fields of applications, including location-based services, ambient assisted living, line traceability, simultaneous localization and mapping, telemanipulation, and Industry 4.0. The required accuracy depends on the specific application, ranging from a few meters, which are needed to navigate a large mall, to less than 1 cm, required for biometrics and telemanipulation.

As a result of the reduced effectiveness of Global Navigation Satellite Systems (GNSS) in indoor environments, several IPS techniques were developed over the years. The approaches mentioned in the literature include image recognition techniques, inertial measurements, and the measurement of specific parameters of different signals, including ultrasounds, radio frequency waves, or magnetic fields. Various parameters may be measured, such as the direction of arrival, time domain quantities, and received signal strength.

Positioning or tracking is then performed using digital signal processing. As each measurement principle introduces specific challenges, various methods have been proposed, and possibly jointly used. They include fitting the measurements to a signal propagation model with respect to the known position anchors, sensor fusion between heterogeneous measurements, and the usage of locally generated fingerprinting datasets, often coupled with machine learning techniques. The achievable accuracy and the measurement rate depends on the selected measurement principle, on the sensor accuracy, and on the adopted signal processing, leading to tradeoffs between performance, cost, and power consumption, which affect the lifetime of battery powered units.

In this regard, a strong interest is emerging in the methods implemented in smartphones, as these widespread platforms are equipped with multiple sensors, support various RF communication protocols, feature powerful data processors, and may consist of seamless interoperation with GNSS positioning and with applications that exploit positioning results.

This Special Issue targets novel research results for IPTs, focused mostly, but not exclusively, on sensor characteristics, node architecture and connectivity, design tradeoffs, positioning algorithms, and overall positioning and tracking performance.

Prof. Dr. Antonio Moschitta
Prof. Dr. Jörg Blankenbach
Prof. Dr. Domenico Capriglione
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 papers will be 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.

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Keywords

  • Indoor positioning
  • Tracking
  • Navigation
  • Sensors
  • Node and network architecture
  • Sensor fusion
  • Measurement principles
  • Accuracy
 

Published Papers (3 papers)

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Research

Open AccessArticle
3D Multiple Sound Source Localization by Proposed Cuboids Nested Microphone Array in Combination with Adaptive Wavelet-Based Subband GEVD
Electronics 2020, 9(5), 867; https://doi.org/10.3390/electronics9050867 - 23 May 2020
Abstract
Sound source localization is one of the applicable areas in speech signal processing. The main challenge appears when the aim is a simultaneous multiple sound source localization from overlapped speech signals with an unknown number of speakers. Therefore, a method able to estimate [...] Read more.
Sound source localization is one of the applicable areas in speech signal processing. The main challenge appears when the aim is a simultaneous multiple sound source localization from overlapped speech signals with an unknown number of speakers. Therefore, a method able to estimate the number of speakers, along with the speaker’s location, and with high accuracy is required in real-time conditions. The spatial aliasing is an undesirable effect of the use of microphone arrays, which decreases the accuracy of localization algorithms in noisy and reverberant conditions. In this article, a cuboids nested microphone array (CuNMA) is first proposed for eliminating the spatial aliasing. The CuNMA is designed to receive the speech signal of all speakers in different directions. In addition, the inter-microphone distance is adjusted for considering enough microphone pairs for each subarray, which prepares appropriate information for 3D sound source localization. Subsequently, a speech spectral estimation method is considered for evaluating the speech spectrum components. The suitable spectrum components are selected and the undesirable components are denied in the localization process. The speech information is different in frequency bands. Therefore, the adaptive wavelet transform is used for subband processing in the proposed algorithm. The generalized eigenvalue decomposition (GEVD) method is implemented in sub-bands on all nested microphone pairs, and the probability density function (PDF) is calculated for estimating the direction of arrival (DOA) in different sub-bands and continuing frames. The proper PDFs are selected by thresholding on the standard deviation (SD) of the estimated DOAs and the rest are eliminated. This process is repeated on time frames to extract the best DOAs. Finally, K-means clustering and silhouette criteria are considered for DOAs classification in order to estimate the number of clusters (speakers) and the related DOAs. All DOAs in each cluster are intersected for estimating the position of the 3D speakers. The closest point to all DOA planes is selected as a speaker position. The proposed method is compared with a hierarchical grid (HiGRID), perpendicular cross-spectra fusion (PCSF), time-frequency wise spatial spectrum clustering (TF-wise SSC), and spectral source model-deep neural network (SSM-DNN) algorithms based on the accuracy and computational complexity of real and simulated data in noisy and reverberant conditions. The results show the superiority of the proposed method in comparison with other previous works. Full article
(This article belongs to the Special Issue Indoor Positioning Techniques)
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Open AccessFeature PaperArticle
Multi-Sensor Accurate Forklift Location and Tracking Simulation in Industrial Indoor Environments
Electronics 2019, 8(10), 1152; https://doi.org/10.3390/electronics8101152 - 12 Oct 2019
Cited by 1
Abstract
Location and tracking needs are becoming more prominent in industrial environments nowadays. Process optimization, traceability or safety are some of the topics where a positioning system can operate to improve and increase the productivity of a factory or warehouse. Among the different options, [...] Read more.
Location and tracking needs are becoming more prominent in industrial environments nowadays. Process optimization, traceability or safety are some of the topics where a positioning system can operate to improve and increase the productivity of a factory or warehouse. Among the different options, solutions based on ultra-wideband (UWB) have emerged during recent years as a good choice to obtain highly accurate estimations in indoor scenarios. However, the typical harsh wireless channel conditions found inside industrial environments, together with interferences caused by workers and machinery, constitute a challenge for this kind of system. This paper describes a real industrial problem (location and tracking of forklift trucks) that requires precise internal positioning and presents a study on the feasibility of meeting this challenge using UWB technology. To this end, a simulator of this technology was created based on UWB measurements from a set of real sensors. This simulator was used together with a location algorithm and a physical model of the forklift to obtain estimations of position in different scenarios with different obstacles. Together with the simulated UWB sensor, an additional inertial sensor and optical sensor were modeled in order to test its effect on supporting the location based on UWB. All the software created for this work is published under an open-source license and is publicly available. Full article
(This article belongs to the Special Issue Indoor Positioning Techniques)
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Open AccessArticle
Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application
Electronics 2019, 8(5), 554; https://doi.org/10.3390/electronics8050554 - 17 May 2019
Cited by 4
Abstract
In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained [...] Read more.
In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices. Full article
(This article belongs to the Special Issue Indoor Positioning Techniques)
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