Multisensor navigation systems: a remedy for GNSS vulnerabilities?

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required

Verification of Localization via Blockchain Technology on Unmanned Aerial Vehicle Swarm

Verification of the geographic location of a moving device is vital. This verification is important in terms of ensuring that the flying systems moving in the swarm are in orbit and that they are able to task completion and manage their energy efficiency. Cyber-attacks on unmanned aerial vehicles (UAV) in a swarm can affect their position and cause various damages. In order to avoid this challenge, it is necessary to share with each other the positions of UAV in the swarm and to increase their accuracy. In this study, it is aimed to increase position accuracy and data integrity of UAV by using blockchain technology in swarm. Experiments were conducted on a virtual UAV network (UAVNet). Successful results were obtained from this proposed study

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Entwicklung intelligenter GNSS-basierten Landfahrzeug Lokalisierungssysteme

The usage of Global Navigation Satellites Systems (GNSS) for localisation purposes demands a permanent evaluation of the position information provided for the receiver, as well as a standardised GNSS-Receivers validation methodology and subsequently quality control procedures oriented to land vehicles within the ergodic hypothesis. The use of an independent reference system should provide enough information to validate the localisation system, but the lack of proper evaluation and procedures presents significant blind spots for future applications in both the GNSS-Receiver and the correspondent reference system. To solve these problems an approach based on artificial intelligence (AI) is presented. Also the development of an advanced filter technique for positioning estimation results in significant improvements of the reference system, even allowing a standalone GNSSdependent reference system when no independent systems are available. The presented developments are the bases for future intelligent GNSS-based localisation systems. The methodologies combine the advanced Particle Filter (PF) for positioning estimation with the newly developed Mahalanobis Ellipses Filter (MEF) methodology for accuracy-based data evaluation and the Artificial Neural Networks (ANN) models for both quantitative and qualitative validation. In this thesis the bases of the intelligent GNSS-based localisation system are presented and developed follows the BMW principle. In German the BMW principle stands for Beschreibungsmittel (means of description), Methode (methods) and Werkzeug (tool). The resulting system described along the thesis is applied and tested in a demonstrator tool, validating the developed methodologies in both software and hardware level. The proposed methodologies for the development of an intelligent GNSS-based localisation system are a substantial contribution for intelligent GNSS-based validation tools that will enable future safety-relevant applications, in field such as on-board uncertainty evaluation of vehicle localisation; advanced driver assistance systems; and GNSS-based vehicle localisation with intelligent maps for track selective enabled-localisation.Die Nutzung der globalen Navigationssatellitensysteme (GNSS) zu Lokalisierungszwecken erfordert eine ständige Auswertung der generierten Positionsinformationen sowie eine standardisierte Validierungsmethodik und anschließende Qualitätskontrollverfahren der GNSS-Empfänger. Die Verwendung eines unabhängigen Referenzsystems sollte genügend Informationen liefern, um das Lokalisierungssystem zu validieren, aber das Fehlen sowohl einer angemessenen Auswertung als auch entsprechender Verfahren stellen erhebliche Lücken für zukünftige Anwendungen sowohl dem Empfänger und der Referenz dar. Um diese Probleme zu lösen, wird ein Ansatz mit Künstlicher Intelligenz (KI) vorgestellt. Die Entwicklung KI-basierter Validierungstools sowie Filtertechniken zur Positionsbestimmung, um das Bezugssystem zu unterstützen, führt zu erheblichen Verbesserungen insofern, als dass ein GNSS-abhängiges Referenzsystem erstellt werden kann, wenn keine unabhängigen Referenzsysteme verfügbar sein sollten. Diese zusätzlichen Elemente sind die Grundlagen für zukünftige intelligente GNSSbasierte Lokalisierungssysteme. Die vorgestellten Methoden vereinen fortschrittliche Partikelfilter (PF) für die Positionsbestimmung mit der neuentwickelten Mahalanobis-Ellipsen-Filter (MEF)-Methodik für die genauigkeitsbasierte Datenauswertung, sowie einen Künstlichen-Neuronalen-Netze (KNN)-Ansatz für sowohl qualitative als auch quantitative Validierungstools. Im Rahmen des BMW-Prinzips (kurz für Beschreibungsmittel, Methoden undWerkzeuge) werden die Grundlagen für ein KI-basiertes System für GNSS-basierte Lokalisierungssysteme vorgestellt und im Rahmen dieser Arbeit entwickelt. Das sich ergebende intelligente GNSS-basierte Lokalisierungssystem wird in einem Demonstrator-Werkzeug angewendet, um das entwickelte System auf der Software- und Hardware-Ebene zu validieren. Abschließend wird eine Risikoanalyse des Demonstrators präsentiert. Diese Methoden zur Entwicklung eines intelligenten GNSS-basierten Lokalisierungssystems werden zukünftige sicherheitsrelevante Anwendungen in Bereichen wie Bordunsicherheitsermittlung in der Fahrzeuglokalisierung, Fahrassistenzsysteme und GNSSbasierte Fahrzeugortung mit intelligenten Karten für eine spurselektive Lokalisierung ermöglichen

Reef rescue marine monitoring program quality assurance and quality control manual 2013/2014

The Reef Rescue Marine Monitoring Program Quality Assurance and Quality Control (QA/QC) Manual summarises the monitoring methods and procedures used in the Program. Detailed sampling manuals, standard operating procedures, analytical procedures and other details are provided as appendices

A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions

One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In addition, some of these surveys focused on the Transmission Control Protocol/Internet Protocol (TCP/IP) model, which does not differentiate between the application, session, and presentation and the data link and physical layers of the Open System Interconnection (OSI) model. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions

Developing GPS river flow tracers (GRiFTers) to investigate large scale river flow phenomena

Existing flow measurement methods in natural gravel rivers are largely based on a series of point measurements detached from the dynamic nature of river flow. Traditional measurement methods are limited in many environments and locations due to an inability to access directly the channel; this situation is further complicated at high discharges where entry into the channel becomes impossible. The inadequacy of currently utilised flow measurement methods is highlighted in the study of riffle-pool sequences where limited data has produced gaps in the understanding of these fundamentally important bedform structures. Within the study of riffle-pool sequences the most prominent debates concern the precise means of sequence development and maintenance, the existence / operation of the velocity reversal hypothesis and the spatial compositions and periodicity of these quasi-regular bedform features.The expanding usage of remote sensor monitoring techniques, the incorporation of GPS receivers into drifters to provide improved positioning, and the adaptation of drifters for use in the surf zone and in estuaries and lakes have combined to highlight the potential of producing a GPS river flow tracer (GRiFTer). The development of a GRiFTer suitable for deployment in a natural gravel bed river system is described whilst the logistics of performing a field based GRiFTer investigation, data acquisition and analysis methods and the achievable accuracy of the approach are also considered.The development of a GPS River Flow Tracer provides an innovative approach to the acquisition of surface velocity measurements through the development of a series of GRiFTer based analysis tools and techniques. The suite of tools developed to date includes; the ability to measure a single primary flowline through a reach, a means of independently measuring the effective width of channel flow, the identification of low velocity zones (and the direction of flow within these areas), three different methods for the measurement of surface flow velocity (primary flowline, cross-sectional averaged and reach scale) and a means of defining riffles and pools from the relationship between depth and surface flow velocities.The study ultimately concludes with a conceptual model for the development and maintenance of riffle-pool sequences based on an adaptation of the flow convergence routing hypothesis

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Aeronautical Engineering: A Continuing Bibliography with Indexes

This report lists reports, articles and other documents recently announced in the NASA STI Database