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Special Issue "Selected Papers from 9th International Conference on Localization and GNSS 2019 (ICL-GNSS 2019)"

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

Deadline for manuscript submissions: closed (31 October 2019).

Special Issue Editor

Prof. Dr. Jari Nurmi
Website SciProfiles
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 and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The 9th International Conference on Localization and GNSS (ICL-GNSS 2019) (http://www.icl-gnss.org/2019/) will take place in Nuremberg, Germany on 4—6 June 2019.

Reliable navigation and positioning are becoming essential in applications of IoT in industry and logistic applications, in smart city environments, for safety-critical purposes, and in public services and consumer products to guarantee transparent, efficient, and reliable workflows. A robust localization solution is needed, which will be available continuously regardless of whether it is implemented outdoor or indoor or different platforms. ICL-GNSS addresses the latest research on wireless and satellite-based positioning techniques to provide reliable and accurate position information with low latency. The emphasis is on the design of mass-market navigation receivers and related tools and methodologies.

Authors of the selected papers related to Sensors from the conference are invited to submit the extended versions of their original papers.

Prof. 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 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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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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

  • Global navigation satellite systems
  • Wireless positioning
  • Jamming and spoofing
  • Cooperative positioning
  • Multi-GNSS receivers
  • Hybrid positioning
  • Indoor positioning
  • Self-driving cars
  • Autonomous vehicles
  • Unmanned aerial vehicles
  • Sensor fusion

Published Papers (8 papers)

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Research

Open AccessArticle
Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part II
Sensors 2020, 20(7), 1806; https://doi.org/10.3390/s20071806 - 25 Mar 2020
Abstract
Global Navigation Satellite System (GNSS) meaconing and spoofing are being considered as the key threats to the Safety-of-Life (SoL) applications that mostly rely upon the use of open service (OS) signals without signal or data-level protection. While a number of pre and post [...] Read more.
Global Navigation Satellite System (GNSS) meaconing and spoofing are being considered as the key threats to the Safety-of-Life (SoL) applications that mostly rely upon the use of open service (OS) signals without signal or data-level protection. While a number of pre and post correlation techniques have been proposed so far, possible utilization of the supervised machine learning algorithms to detect GNSS meaconing and spoofing is currently being examined. One of the supervised machine learning algorithms, the Support Vector Machine classification (C-SVM), is proposed for utilization at the GNSS receiver level due to fact that at that stage of signal processing, a number of measurements and observables exists. It is possible to establish the correlation pattern among those GNSS measurements and observables and monitor it with use of the C-SVM classification, the results of which we present in this paper. By adding the real-world spoofing and meaconing datasets to the laboratory-generated spoofing datasets at the training stage of the C-SVM, we complement the experiments and results obtained in Part I of this paper, where the training was conducted solely with the use of laboratory-generated spoofing datasets. In two experiments presented in this paper, the C-SVM algorithm was cross-fed with the real-world meaconing and spoofing datasets, such that the meaconing addition to the training was validated by the spoofing dataset, and vice versa. The comparative analysis of all four experiments presented in this paper shows promising results in two aspects: (i) the added value of the training dataset enrichment seems to be relevant for real-world GNSS signal manipulation attempt detection and (ii) the C-SVM-based approach seems to be promising for GNSS signal manipulation attempt detection, as well as in the context of potential federated learning applications. Full article
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Open AccessArticle
Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I
Sensors 2020, 20(4), 1171; https://doi.org/10.3390/s20041171 - 20 Feb 2020
Cited by 1
Abstract
The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user’s system being [...] Read more.
The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user’s system being aware, resulting in presented hazardous misleading information and signal integrity deterioration without an alarm being triggered. Among the number of proposed spoofing detection and mitigation techniques applied at different stages of the signal processing, we present a method for the cross-correlation monitoring of multiple and statistically significant GNSS observables and measurements that serve as an input for the supervised machine learning detection of potentially spoofed or meaconed GNSS signals. The results of two experiments are presented, in which laboratory-generated spoofing signals are used for training and verification within itself, while two different real-world spoofing and meaconing datasets were used for the validation of the supervised machine learning algorithms for the detection of the GNSS spoofing and meaconing. Full article
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Open AccessArticle
Environmental Monitoring with Distributed Mesh Networks: An Overview and Practical Implementation Perspective for Urban Scenario
Sensors 2019, 19(24), 5548; https://doi.org/10.3390/s19245548 - 16 Dec 2019
Cited by 1
Abstract
Almost inevitable climate change and increasing pollution levels around the world are the most significant drivers for the environmental monitoring evolution. Recent activities in the field of wireless sensor networks have made tremendous progress concerning conventional centralized sensor networks known for decades. However, [...] Read more.
Almost inevitable climate change and increasing pollution levels around the world are the most significant drivers for the environmental monitoring evolution. Recent activities in the field of wireless sensor networks have made tremendous progress concerning conventional centralized sensor networks known for decades. However, most systems developed today still face challenges while estimating the trade-off between their flexibility and security. In this work, we provide an overview of the environmental monitoring strategies and applications. We conclude that wireless sensor networks of tomorrow would mostly have a distributed nature. Furthermore, we present the results of the developed secure distributed monitoring framework from both hardware and software perspectives. The developed mechanisms provide an ability for sensors to communicate in both infrastructure and mesh modes. The system allows each sensor node to act as a relay, which increases the system failure resistance and improves the scalability. Moreover, we employ an authentication mechanism to ensure the transparent migration of the nodes between different network segments while maintaining a high level of system security. Finally, we report on the real-life deployment results. Full article
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Open AccessArticle
UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis
Sensors 2019, 19(24), 5547; https://doi.org/10.3390/s19245547 - 16 Dec 2019
Cited by 2
Abstract
Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical [...] Read more.
Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system. Full article
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Open AccessArticle
Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver
Sensors 2019, 19(24), 5439; https://doi.org/10.3390/s19245439 - 10 Dec 2019
Abstract
Spoofing of global navigation satellite system (GNSS) signals threatens positioning systems. A counter-method is to detect the presence of spoofed signals, followed by a warning to the user. In this paper, a multi-antenna snapshot receiver is presented to detect the presence of a [...] Read more.
Spoofing of global navigation satellite system (GNSS) signals threatens positioning systems. A counter-method is to detect the presence of spoofed signals, followed by a warning to the user. In this paper, a multi-antenna snapshot receiver is presented to detect the presence of a spoofing attack. The spatial similarities of the array steering vectors are analyzed, and different metrics are used to establish possible detector functions. These include subset methods, Eigen-decomposition, and clustering algorithms. The results generated within controlled spoofing conditions show that a spoofed constellation of GNSS satellites can be successfully detected. The derived system-level detectors increase performance in comparison to pair-wise methods. A controlled test setup achieved perfect detection; however, in real-world cases, the performance would not be as ideal. Some detection metrics and features for blind spoofing detecting, with an array of antennas, are identified, which opens the field for future advanced multi-detector developments. Full article
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Open AccessArticle
The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines
Sensors 2019, 19(23), 5219; https://doi.org/10.3390/s19235219 - 28 Nov 2019
Cited by 1
Abstract
Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, [...] Read more.
Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time. Full article
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Open AccessArticle
An Empirical Study on V2X Enhanced Low-Cost GNSS Cooperative Positioning in Urban Environments
Sensors 2019, 19(23), 5201; https://doi.org/10.3390/s19235201 - 27 Nov 2019
Cited by 1
Abstract
High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on [...] Read more.
High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm’s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments. Full article
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Open AccessArticle
Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates
Sensors 2019, 19(21), 4802; https://doi.org/10.3390/s19214802 - 05 Nov 2019
Cited by 3
Abstract
This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as [...] Read more.
This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér–Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér–Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of 0 . 8 m at 90% confidence. Full article
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