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Selected Papers from 12th International Conference on Localization and GNSS 2022 (ICL-GNSS 2022)

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 13843

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 12th International Conference on Localization and GNSS (ICL-GNSS 2022) (https://events.tuni.fi/icl-gnss2022/) will be held on 7–9 June 2022 in Tampere, Finland.

Reliable navigation and positioning are becoming essential in applications of the IoT, including in industry and logistic applications, in smart city environments, for safety-critical purposes, and in public services and consumer products, in order to guarantee transparent, efficient, and reliable workflows. A robust localization solution is urgently required, which will be continuously available, regardless of whether it is implemented outdoors or indoors, or on 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. Emphasis is placed on the design of mass-market navigation receivers and related tools and methodologies; however, 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.

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Published Papers (5 papers)

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Research

30 pages, 5822 KiB  
Article
Positioning by Multicell Fingerprinting in Urban NB-IoT Networks
by Luca De Nardis, Giuseppe Caso, Özgü Alay, Marco Neri, Anna Brunstrom and Maria-Gabriella Di Benedetto
Sensors 2023, 23(9), 4266; https://doi.org/10.3390/s23094266 - 25 Apr 2023
Cited by 5 | Viewed by 2519
Abstract
Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and [...] Read more.
Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments. Full article
(This article belongs to the Special Issue Selected Papers from 12th International Conference on Localization and GNSS 2022 (ICL-GNSS 2022))
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25 pages, 2928 KiB  
Article
Evaluation of Low-Complexity Adaptive Full Direct-State Kalman Filter for Robust GNSS Tracking
by Iñigo Cortés, Johannes Rossouw van der Merwe, Elena Simona Lohan, Jari Nurmi and Wolfgang Felber
Sensors 2023, 23(7), 3658; https://doi.org/10.3390/s23073658 - 31 Mar 2023
Cited by 5 | Viewed by 2098
Abstract
This paper evaluates the implementation of a low-complexity adaptive full direct-state Kalman filter (DSKF) for robust tracking of global navigation satellite system (GNSS) signals. The full DSKF includes frequency locked loop (FLL), delay locked loop (DLL), and phase locked loop (PLL) tracking schemes. [...] Read more.
This paper evaluates the implementation of a low-complexity adaptive full direct-state Kalman filter (DSKF) for robust tracking of global navigation satellite system (GNSS) signals. The full DSKF includes frequency locked loop (FLL), delay locked loop (DLL), and phase locked loop (PLL) tracking schemes. The DSKF implementation in real-time applications requires a high computational cost. Additionally, the DSKF performance decays in time-varying scenarios where the statistical distribution of the measurements changes due to noise, signal dynamics, multi-path, and non-line-of-sight effects. This study derives the full lookup table (LUT)-DSKF: a simplified full DSKF considering the steady-state convergence of the Kalman gain. Moreover, an extended version of the loop-bandwidth control algorithm (LBCA) is presented to adapt the response time of the full LUT-DSKF. This adaptive tracking technique aims to increase the synchronization robustness in time-varying scenarios. The proposed tracking architecture is implemented in an GNSS hardware receiver with an open software interface. Different configurations of the adaptive full LUT-DSKF are evaluated in simulated scenarios with different dynamics and noise cases for each implementation. The results confirm that the LBCA used in the FLL-assisted-PLL (FAP) is essential to maintain a position, velocity, and time (PVT) fix in high dynamics. Full article
(This article belongs to the Special Issue Selected Papers from 12th International Conference on Localization and GNSS 2022 (ICL-GNSS 2022))
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32 pages, 5120 KiB  
Article
Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System
by Johannes Rossouw van der Merwe, David Contreras Franco, Jonathan Hansen, Tobias Brieger, Tobias Feigl, Felix Ott, Dorsaf Jdidi, Alexander Rügamer and Wolfgang Felber
Sensors 2023, 23(7), 3452; https://doi.org/10.3390/s23073452 - 25 Mar 2023
Cited by 8 | Viewed by 3949
Abstract
Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, [...] Read more.
Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware. Full article
(This article belongs to the Special Issue Selected Papers from 12th International Conference on Localization and GNSS 2022 (ICL-GNSS 2022))
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27 pages, 739 KiB  
Article
Direction of Arrival Method for L-Shaped Array with RF Switch: An Embedded Implementation Perspective
by Tiago Troccoli, Juho Pirskanen, Jari Nurmi, Aleksandr Ometov, Jorge Morte, Elena Simona Lohan and Ville Kaseva
Sensors 2023, 23(6), 3356; https://doi.org/10.3390/s23063356 - 22 Mar 2023
Cited by 3 | Viewed by 2947
Abstract
This paper addresses the challenge of implementing Direction of Arrival (DOA) methods for indoor localization using Internet of Things (IoT) devices, particularly with the recent direction-finding capability of Bluetooth. DOA methods are complex numerical methods that require significant computational resources and can quickly [...] Read more.
This paper addresses the challenge of implementing Direction of Arrival (DOA) methods for indoor localization using Internet of Things (IoT) devices, particularly with the recent direction-finding capability of Bluetooth. DOA methods are complex numerical methods that require significant computational resources and can quickly deplete the batteries of small embedded systems typically found in IoT networks. To address this challenge, the paper presents a novel Unitary R-D Root MUSIC for L-shaped arrays that is tailor-made for such devices utilizing a switching protocol defined by Bluetooth. The solution exploits the radio communication system design to speed up execution, and its root-finding method circumvents complex arithmetic despite being used for complex polynomials. The paper carries out experiments on energy consumption, memory footprint, accuracy, and execution time in a commercial constrained embedded IoT device series without operating systems and software layers to prove the viability of the implemented solution. The results demonstrate that the solution achieves good accuracy and attains an execution time of a few milliseconds, making it a viable solution for DOA implementation in IoT devices. Full article
(This article belongs to the Special Issue Selected Papers from 12th International Conference on Localization and GNSS 2022 (ICL-GNSS 2022))
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15 pages, 1071 KiB  
Article
Analysis of Spatial Decorrelation of Small-Scale Tropospheric Delay Using High-Resolution NWP Data
by Jan Erik Håkegård, Nadezda Sokolova and Aiden Morrison
Sensors 2023, 23(3), 1237; https://doi.org/10.3390/s23031237 - 21 Jan 2023
Cited by 1 | Viewed by 1192
Abstract
This paper contains results from a study where numerical weather product (NWP) data provided by MET Norway were used to estimate the differential zenith tropospheric delay (dZTD) for an area including Scandinavia, Finland and the Baltic countries. The NWP data have a high [...] Read more.
This paper contains results from a study where numerical weather product (NWP) data provided by MET Norway were used to estimate the differential zenith tropospheric delay (dZTD) for an area including Scandinavia, Finland and the Baltic countries. The NWP data have a high spatial resolution of 2.5×2.5 km, and the estimated dZTD for the grid positions allows for calculation of the tropospheric gradient on short baselines. The results give an indication of how large dZTD values for baselines of up to 20 km can be, and of where the largest events are located within the coverage area. One year of data were processed, and dZTD values up to 18 cm with baselines were detected. Preliminary results comparing the NWP-based results with GNSS-based results are included. The motivation for this investigation was to better understand the characteristics of this phenomenon as a preamble to a later investigation of how it might impact GNSS-based navigation systems with integrity support in these regions. Full article
(This article belongs to the Special Issue Selected Papers from 12th International Conference on Localization and GNSS 2022 (ICL-GNSS 2022))
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