1,536 research outputs found
First results of a GNSS-R experiment from a stratospheric balloon over boreal forests
The empirical results of a global navigation satellite systems reflectometry (GNSS-R) experiment onboard the Balloon EXperiments for University Students (BEXUS) 17 stratospheric balloon performed north of Sweden over boreal forests show that the power of the reflected signals is nearly independent of the platform height for a high coherent integration time T-c = 20 ms. This experimental evidence shows a strong coherent component in the forward scattered signal, as compared with the incoherent component, that allows to be tracked. The bistatic coherent reflectivity is also evaluated as a function of the elevation angle, showing a decrease of similar to 6 dB when the elevation angle increases from 35. to 70 degrees. The received power presents a clearly multimodal behavior, which also suggests that the coherent scattering component may be taking place in different forest elements, i.e., soil, canopy, and through multiple reflections canopy-soil and soil-trunk. This experiment has provided the first GNSS-R data set over boreal forests. The evaluation of these results can be useful for the feasibility study of this technique to perform biomass monitoring that is a key factor to analyze the carbon cycle.Peer ReviewedPostprint (author's final draft
Cooperative environment recognition utilizing UWB waveforms and CNNs
Cooperative navigation enhances localization performance and situational awareness in challenging conditions, such as in tactical and first responder operations. In this work we demonstrate how the waveform of the Ultra Wideband (UWB) signal used for ranging in cooperative navigation can also be used to detect the environment surrounding the user of the navigation system. Different environments affect the wave-form in different ways, and thus the received waveform contains features characteristic to the environment around the receiver. We show how the received UWB signal waveform can be used in a Convolutional Neural Network (CNN) in order to determine whether the user is outdoors, indoors or in a forest. The environment is recognized correctly more than 90% of the time. © 2020 German Institute of Navigation - DGON.Peer reviewe
Satellite Navigation for the Age of Autonomy
Global Navigation Satellite Systems (GNSS) brought navigation to the masses.
Coupled with smartphones, the blue dot in the palm of our hands has forever
changed the way we interact with the world. Looking forward, cyber-physical
systems such as self-driving cars and aerial mobility are pushing the limits of
what localization technologies including GNSS can provide. This autonomous
revolution requires a solution that supports safety-critical operation,
centimeter positioning, and cyber-security for millions of users. To meet these
demands, we propose a navigation service from Low Earth Orbiting (LEO)
satellites which deliver precision in-part through faster motion, higher power
signals for added robustness to interference, constellation autonomous
integrity monitoring for integrity, and encryption / authentication for
resistance to spoofing attacks. This paradigm is enabled by the 'New Space'
movement, where highly capable satellites and components are now built on
assembly lines and launch costs have decreased by more than tenfold. Such a
ubiquitous positioning service enables a consistent and secure standard where
trustworthy information can be validated and shared, extending the electronic
horizon from sensor line of sight to an entire city. This enables the
situational awareness needed for true safe operation to support autonomy at
scale.Comment: 11 pages, 8 figures, 2020 IEEE/ION Position, Location and Navigation
Symposium (PLANS
Indoor location based services challenges, requirements and usability of current solutions
Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge
Detection of GNSS Ionospheric Scintillations based on Machine Learning Decision Tree
This paper proposes a methodology for automatic, accurate and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz post-correlation data provided by a GNSS receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts
UAVs for the Environmental Sciences
This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application
Intrusion Detection System for Platooning Connected Autonomous Vehicles
The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks
Evaluating airborne laser data on steeply sloping terrain
Accuracy of Airborne Laser Terrain Mapping (ALTM) elevations is not well known on steeply sloping terrain. A unique method was used whereby, the planimetric location of ALTM ground strikes were located in the field and reference elevations measured at these points. Survey-grade Global Navigation Satellite System (GNSS) and rigorous techniques accurately established vertical heights to 0.010 meters, Root Mean Squared Error (RMSE). Sampled slopes range from 0.5 degrees to 50.6 degrees. A positive quadratic relationship exists between slope and vertical error. Error is negligible on slopes less than twenty degrees. Incidence angle, footprint size, and elevation spread from the upper reach of the footprint to the lower reach for each laser strike were also determined. An increase in each results in an increase in ALTM elevation imprecision. Elevation spread within the footprint and horizontal error could account for high percentages of vertical error on steeper slopes
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
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