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Selected Papers from 11th International Conference on Localization and GNSS...
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Selected Papers from 11th International Conference on Localization and GNSS 2021 (ICL-GNSS 2021)

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 7265

Special Issue Editor

Prof. Dr. Jari Nurmi

E-Mail Website
Guest Editor
Electrical Engineering, Tampere University, Tampere, Finland
Interests: GNSS receiver architecture and implementation; multi-technology positioning; software-defined radio for communications and positioning; cognitive and cooperative positioning; IoT and embedded systems; reconfigurable and adaptable systems; approximate computing in particular in the receiver baseband domain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 11th International Conference on Localization and GNSS (ICL-GNSS 2021) (https://events.tuni.fi/icl-gnss2021/) will take place in Tampere, Finland, on 1–3 June 2021.

Reliable navigation and positioning are becoming essential in applications of the IoT in industry and logistic applications, in smart city environments, for safety-critical purposes, and in public services and consumer products for guaranteeing transparent, efficient, and reliable workflows. A robust localization solution is needed, which will be available continuously regardless of whether it is implemented outdoors or indoors or in different platforms. ICL-GNSS addresses the latest research on wireless and satellite-based positioning techniques for providing reliable and accurate position information with low latency. The emphasis is on the design of mass-market navigation receivers and related tools and methodologies, but many types of sensing devices, wireless systems with localization capabilities, and location-aware applications are within the scope of the Special Issue.

The authors of the selected papers related to sensors from the conference are invited to submit the extended versions of their original papers.

Prof. Dr. Jari Nurmi
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.

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Published Papers (3 papers)

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Research

25 pages, 22052 KiB  
Article
A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly
by Aditi Site, Saigopal Vasudevan, Samuel Olaiya Afolaranmi, Jose L. Martinez Lastra, Jari Nurmi and Elena Simona Lohan
Sensors 2022, 22(13), 4946; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134946 - 30 Jun 2022
Cited by 10 | Viewed by 1981
Abstract
Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the [...] Read more.
Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feelings of loneliness is mobility. In this paper, we present a machine-learning based approach to classify the user loneliness levels using their indoor and outdoor mobility patterns. User mobility data has been collected based on indoor and outdoor sensors carried on by volunteers frequenting an elderly nursing house in Tampere region, Finland. The data was collected using Pozyx sensor for indoor data and Pico minifinder sensor for outdoor data. Mobility patterns such as the distance traveled indoors and outdoors, indoor and outdoor estimated speed, and frequently visited clusters were the most relevant features for classifying the user’s perceived loneliness levels.Three types of data used for classification task were indoor data, outdoor data and combined indoor-outdoor data. Indoor data consisted of indoor mobility data and statistical features from accelerometer data, outdoor data consisted of outdoor mobility data and other parameters such as speed recorded from sensors and course of a person whereas combined indoor-outdoor data had common mobility features from both indoor and outdoor data. We found that the machine-learning model based on XGBoost algorithm achieved the highest performance with accuracy between 90% and 98% for indoor, outdoor, and combined indoor-outdoor data. We also found that Lubben-scale based labelling of perceived loneliness works better for both indoor and outdoor data, whereas UCLA scale-based labelling works better with combined indoor-outdoor data. Full article
(This article belongs to the Special Issue Selected Papers from 11th International Conference on Localization and GNSS 2021 (ICL-GNSS 2021))
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27 pages, 2676 KiB  
Article
Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control
by Johannes Rossouw van der Merwe, Fabio Garzia, Alexander Rügamer, Santiago Urquijo, David Contreras Franco and Wolfgang Felber
Sensors 2022, 22(2), 679; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020679 - 16 Jan 2022
Cited by 5 | Viewed by 1919
Abstract
The performance of global navigation satellite system (GNSS) receivers is significantly affected by interference signals. For this reason, several research groups have proposed methods to mitigate the effect of different kinds of jammers. One effective method for wide-band interference mitigation (IM) is the [...] Read more.
The performance of global navigation satellite system (GNSS) receivers is significantly affected by interference signals. For this reason, several research groups have proposed methods to mitigate the effect of different kinds of jammers. One effective method for wide-band interference mitigation (IM) is the high-rate DFT-based data manipulator (HDDM) pulse blanker (PB). It provides good performance to pulsed and frequency sparse interference. However, it and many other methods have poor performance against wide-band noise signals, which are not frequency-sparse. This article proposes to include automatic gain control (AGC) in the HDDM structure to attenuate the signal instead of removing it: the HDDM-AGC. It overcomes the wide-band noise limitation for IM at the cost of limiting mitigation capability to other signals. Previous studies with this approach were limited to only measuring the carrier-to-noise density ratio (C/N0) performance of tracking, but this article extends the analysis to include the impact of the HDDM-AGC algorithm on the position, velocity, and time (PVT) solution. It allows an end-to-end evaluation and impact assessment of mitigation to a GNSS receiver. This study compares two commercial receivers: one high-end and one low-cost, with and without HDDM IM against laboratory-generated interference signals. The results show that the HDDM-AGC provides a PVT availability and precision comparable to high-end commercial receivers with integrated mitigation for most interference types. For pulse interferences, its performance is superior. Further, it is shown that degradation is minimized against wide-band noise interferences. Regarding low-cost receivers, the PVT availability can be increased up to 40% by applying an external HDDM-AGC. Full article
(This article belongs to the Special Issue Selected Papers from 11th International Conference on Localization and GNSS 2021 (ICL-GNSS 2021))
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24 pages, 1868 KiB  
Article
Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter
by Iñigo Cortés, Johannes Rossouw van der Merwe, Elena Simona Lohan, Jari Nurmi and Wolfgang Felber
Sensors 2022, 22(2), 420; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020420 - 06 Jan 2022
Cited by 9 | Viewed by 2073
Abstract
This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF [...] Read more.
This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In time-varying scenarios, the measurements’ distribution changes over time due to noise, signal dynamics, multipath, and non-line-of-sight effects. These kinds of scenarios make difficult the search for a suitable measurement and process noise model, leading to a sub-optimal solution of the DSKF. The loop-bandwidth control algorithm (LBCA) can adapt the DSKF according to the time-varying scenario and improve its performance significantly. This study introduces two methods to adapt the DSKF using the LBCA: The LBCA-based DSKF and the LBCA-based lookup table (LUT)-DSKF. The former method adapts the steady-state process noise variance based on the LBCA’s loop bandwidth update. In contrast, the latter directly relates the loop bandwidth with the steady-state Kalman gains. The presented techniques are compared with the well-known state-of-the-art carrier-to-noise density ratio (C/N0)-based DSKF. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver’s tracking performance and the system performance are evaluated in simulated scenarios with different dynamics and noise cases. Results confirm that the LBCA can be successfully applied to adapt the DSKF. The LBCA-based LUT-DSKF exhibits superior static and dynamic system performance compared to other adaptive tracking techniques using the DSKF while achieving the lowest complexity. Full article
(This article belongs to the Special Issue Selected Papers from 11th International Conference on Localization and GNSS 2021 (ICL-GNSS 2021))
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