Trajectory Clustering for Air Traffic Categorisation

Availability of different types of data and advances in data-driven techniques open the path to more detailed analyses of various phenomena. Here, we examine the insights that can be gained through the analysis of historical flight trajectories, using data mining techniques. The goal is to learn about usual (or nominal) choices airlines make in terms of routing, and their relation with aircraft types and operational flight costs. The clustering is applied to intra-European trajectories during one entire summer season, and a statistical test of independence is used to evaluate the relations between the variables of interest. Even though about half of all flights are less than 1000 km long, and mostly operated by one airline, along one trajectory, the analysis shows that, for longer flights, there exists a clear relation between the trajectory clusters and the operating airlines (in about 49% of city pairs) and/or the aircraft types (30%), and/or the flight costs (45%)

GNSS jamming and its effect on air traffic in Eastern Europe

Global navigation satellite systems technology is at the core of modern air traffic navigation. Aircraft use it to estimate their position, while air navigation service providers rely on services such as automatic dependent surveillance broadcast which have been enabled by this technology. Since satellite signals are very low in power, they are susceptible to radio frequency interference activities, which can have a significant impact on aviation. This paper illustrates how crowd-sourced automatic dependent surveillance data transmitted by aircraft can be used to gain situational awareness about radio frequency interference and how air traffic over Eastern Europe has been impacted by interference activities over a period spanning from February to August 2022. The results suggest that satellite navigation signals were subject to interference of varying strength and duration. We observed several days when more than 1000 flights were affected, representing 60% of the daily traffic in the analysed area. Furthermore, the extent of the interference impact on aviation depends on the altitude of the aircraft, as low-flying aircraft tend to be less affected by interference than the ones flying at higher altitudes. Consequently, this paper contributes to a better understanding of how civil aviation is affected by radio frequency interference and where such disturbances may occur

Engage D2.2 Final Communication and Dissemination Report

This deliverable reports on the communication and dissemination activities carried out by the Engage consortium over the duration of the network. Planned activities have been adapted due to the Covid-19 pandemic, however a full programme of workshops and summer schools has been organised. Support has been given to the annual SESAR Innovation Days conference and there has been an Engage presence at many other events. The Engage website launched in the first month of the network. This was later joined by the Engage ‘knowledge hub’, known as the EngageWiki, which hosts ATM research and knowledge. The wiki provides a platform and consolidated repository with novel user functionality, as well as an additional channel for the dissemination of SESAR results. Engage has also supported and publicised numerous research outputs produced by PhD candidates and catalyst fund projects

ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection

Automatic Dependent Surveillance-Broadcast is an Air Traffic Control system in which aircraft transmit their own information (identity, position, velocity, etc.) to ground sensors for surveillance purposes. This system has many advantages compared to the classical surveillance radars: easy and low-cost implementation, high accuracy of data, and low renewal time, but also limitations: dependency on the Global Navigation Satellite System, a simple unencrypted and unauthenticated protocol. For these reasons, the system is exposed to attacks like jamming/spoofing of the on-board GNSS receiver or false ADS-B messages' injection. After a mathematical model derivation of different types of attacks, we propose the use of a crowd sensor network capable of estimating the Time Difference Of Arrival of the ADS-B messages together with a two-step Kalman filter to detect these attacks (on-board GNSS/ADS-B tampering, false ADS-B message injection, GNSS Spoofing/Jamming). Tests with real data and simulations showed that the algorithm can detect all these attacks with a very high probability of detection and low probability of false alarm

Deep generative modelling of aircraft trajectories in terminal maneuvering areas

Airspace design is subject to a multitude of constraints, which are mainly driven by the concern to keep the risk of mid-air collision below a target level of safety. For that purpose, Monte Carlo simulation methods can be applied to estimate aircraft conflict probability but require the accurate generation of artificial trajectories. Generative models allow to generate an infinite number of trajectories for air traffic procedures where only few observations are available. The generated trajectories must not only resemble observed trajectories in terms of statistical distributions but they should stay flyable and consider uncertainty due to weather, air traffic control, aircraft performances, or human factors. This paper focuses on the generation problem, and its main contribution lies in the adaptation of the Variational Autoencoder structure to the problem of 4-dimensional aircraft trajectories modelling using Temporal Convolutional Networks and a prior distribution composed of a Variational Mixture of Posteriors (VampPrior). The proposed model has been trained on trajectories in the Terminal Manoeuvre Area of Zurich airport, which have a particularly high degree of variability as air traffic controllers often take actions that deviate aircraft from the nominal approach procedure. The model has demonstrated great abilities to take into account such amount of uncertainty. Regarding metrics that evaluate the estimation of the statistical distribution of the observed trajectories, and the flyability of the generated ones, the proposed method outperforms traditional statistical methods by being able to generate more complex and realistic trajectories

Traffic synchronization with controlled time of arrival for cost-efficient trajectories in high-density terminal airspace

The growth in air traffic has led to a continuously growing environmental sensitivity in aviation, encouraging the research into methods for achieving a greener air transportation. In this context, continuous descent operations (CDOs) allow aircraft to follow an optimum flight path that delivers major environmental and economic benefits, giving as a result engine-idle descents from the cruise altitude to right before landing that reduce fuel consumption, pollutant emissions and noise nuisance. However, this type of operations suffers from a well-known drawback: the loss of predictability from the air traffic control (ATC) point of view in terms of overfly times at the different waypoints of the route. In consequence, ATC requires large separation buffers, thus reducing the capacity of the airport. Previous works investigating this issue showed that the ability to meet a controlled time of arrival (CTA) at a metering fix could enable CDOs while simultaneously maintaining airport throughput. In this context, more research is needed focusing on how modern arrival managers (AMANs)—and extended arrival managers (E-AMANs)—could provide support to select the appropriate CTA. ATC would be in charge to provide the CTA to the pilot, who would then use four-dimensional (4D) flight management system (FMS) trajectory management capabilities to satisfy it. A key transformation to achieve a more efficient aircraft scheduling is the use of new air traffic management (ATM) paradigms, such as the trajectory based operations (TBO) concept. This concept aims at completely removing open-loop vectoring and strategic constraints on the trajectories by efficiently implementing a 4D trajectory negotiation process to synchronize airborne and ground equipment with the aim of maximizing both flight efficiency and throughput. The main objective of this PhD thesis is to develop methods to efficiently schedule arrival aircraft in terminal airspace, together with concepts of operations compliant with the TBO concept. The simulated arrival trajectories generated for all the experiments conducted in this PhD thesis, to the maximum possible extent, are considered to be energy-neutral CDOs, seeking to reduce the overall environmental impact of aircraft operations in the ATM system. Ultimately, the objective of this PhD is to achieve a more efficient arrival management of traffic, in which higher levels of predictability and similar levels of capacity are achieved, while the safety of the operations is kept. The designed experiments consider a TBO environment, involving a high synchronization between all the involved actors of the ATM system. Higher levels of automation and information sharing are expected, together with a modernization of both current ATC ground-support tools and aircraft FMSs to comply with the new TBO paradigm.L’increment de tràfic aeri ha portat a una major sensibilitat mediambiental en l’aviació, motivant la recerca en mètodes per aconseguir un transport aeri més ecològic. En aquest context, les operacions de descens continu (CDOs) permeten a les aeronaus seguir una trajectòria que aporta grans beneficis econòmics i ambientals, donant com a resultat descensos amb els motors al ralentí des de l’altitud de creuer fins just abans d’aterrar. Aquestes trajectòries redueixen el consum de combustible, les emissions contaminants i el soroll generat per les aeronaus. No obstant això, aquest tipus d’operacions té un gran desavantatge: la pèrdua de predictibilitat des del punt de vista del controlador aeri (ATC) en termes de temps de pas als diferents punts de la ruta. Com a conseqüència, l’ATC necessita assignar una major separació entre les aeronaus, la qual cosa comporta una reducció en la capacitat de l’aeroport. Estudis previs investigant aquest problema han demostrat que la capacitat de complir amb un temps controlat d’arribada (CTA) a un punt de la ruta (utilitzat per seqüenciar les aeronaus) podria habilitar les CDOs tot mantenint la capacitat de l’aeroport. En aquest context, es necessita investigar més en com els gestors d’arribades (AMANs) i els gestors d’arribades ampliats (E-AMANs) podrien donar suport en la selecció de la CTA més adequada. L’ATC seria l’encarregat d’enviar la CTA al pilot, el qual, per tal de complir amb la CTA, faria servir la capacitat de gestió de trajectòries d’un sistema de gestió de vol (FMS) de quatre dimensions (4D). Una transformació clau per aconseguir una gestió més eficient del tràfic d’arribada és l’ús de nous paradigmes de gestió del tràfic aeri (ATM), com per exemple el concepte d’operacions basades en trajectòries (TBO). Aquest concepte té com a objectiu eliminar completament de les trajectòries la vectorització en “bucle obert” i les restriccions estratègiques. Per aconseguir-ho, es proposa implementar de manera eficient una negociació de la trajectòria 4D, amb l’objectiu de sincronitzar l’equipament de terra amb el de l’aeronau, maximitzant d’aquesta manera l’eficiència dels vols i la capacitat del sistema. El principal objectiu d’aquest doctorat és desenvolupar mètodes per gestionar aeronaus de manera eficient en espai aeri terminal, juntament amb conceptes d’operacions que compleixin amb el concepte de TBO. Les trajectòries d’arribada simulades per tots els experiments definits en aquesta tesi doctoral, en la mesura que s’ha pogut, són CDOs d’energia neutral. D’aquesta manera, la idea és reduir el màxim possible l’impacte mediambiental de les operacions aèries al sistema ATM. En definitiva, l’objectiu d’aquest doctorat és aconseguir una gestió del tràfic d’arribada més eficient, obtenint una major predictibilitat i capacitat, i assegurant que la seguretat de les operacions es manté. Els experiments dissenyats consideren una situació on el concepte de TBO és present, el que comporta una sincronització elevada entre tots els actors implicats en el sistema ATM. Així mateix, s’esperen nivells majors d’automatització i de compartició d’informació, juntament amb una modernització de les eines de suport en terra a l’ATC i dels FMSs de les aeronaus, tot amb l’objectiu de complir amb el nou paradigma de TBO.El incremento de tráfico aéreo ha llevado a una mayor sensibilidad medioambiental en la aviación, motivando la investigación de métodos para conseguir un transporte aéreo más ecológico. En este contexto, las operaciones de descenso continuo (CDOs) permiten a las aeronaves seguir una trayectoria que aporta grandes beneficios económicos y ambientales, dando como resultado descensos con los motores al ralentí desde la altitud de crucero hasta justo antes de aterrizar. Estas trayectorias reducen el consumo de combustible, las emisiones contaminantes y el ruido generado por las aeronaves. No obstante, este tipo de operaciones tiene una gran desventaja: la pérdida de predictibilidad desde el punto de vista del controlador aéreo (ATC) en términos de tiempos de paso en los diferentes puntos de la ruta. Como consecuencia, el ATC necesita asignar una mayor separación entre las aeronaves, lo cual comporta una reducción en la capacidad del aeropuerto. Estudios previos investigando este problema han demostrado que la capacidad de cumplir con un tiempo controlado de llegada (CTA) en un punto de la ruta (utilizado para secuenciar las aeronaves) podría habilitar las CDOs manteniendo al mismo tiempo la capacidad del aeropuerto. En este contexto, es necesario investigar más en cómo los gestores de llegadas (AMANs)—y los gestores de llegadas extendidos (E-AMANs)—podrían dar soporte en la selección de la CTA más adecuada. El ATC sería el encargado de enviar la CTA al piloto, el cual, para cumplir con la CTA, usaría la capacidad de gestión de trayectorias de un sistema de gestión de vuelo (FMS) de cuatro dimensiones (4D). Una transformación clave para conseguir una gestión más eficiente del tráfico de llegada es el uso de nuevos paradigmas de gestión del tráfico aéreo (ATM), como por ejemplo el concepto de operaciones basadas en trayectorias (TBO). Este concepto tiene como objetivo eliminar completamente de las trayectorias la vectorización en “bucle abierto” y las restricciones estratégicas. Para conseguirlo, se propone implementar de manera eficiente una negociación de la trayectoria 4D, con el objetivo de sincronizar el equipamiento de tierra con el de la aeronave, maximizando de esta manera la eficiencia de los vuelos y la capacidad del sistema. El principal objetivo de este doctorado es desarrollar métodos para gestionar aeronaves de manera eficiente en espacio aéreo terminal, junto con conceptos de operaciones que cumplan con el concepto de TBO. Las trayectorias de llegada simuladas para todos los experimentos definidos en esta tesis doctoral, en la medida de lo posible, son CDOs de energía neutra. De esta manera, la idea es reducir lo máximo posible el impacto medioambiental de las operaciones aéreas en el sistema ATM. En definitiva, el objetivo de este doctorado es conseguir una gestión del tráfico de llegada más eficiente, obteniendo una mayor predictibilidad y capacidad, y asegurando que la seguridad de las operaciones se mantiene. Los experimentos diseñados consideran una situación xxi donde el concepto de TBO está presente, lo que comporta una sincronización elevada entre todos los actores implicados en el sistema ATM. Asimismo, se esperan mayores niveles de automatización y de compartición de información, junto con una modernización de las herramientas de soporte en tierra al ATC y de los FMSs de las aeronaves, todo con el objetivo de cumplir con el nuevo paradigma de TBO. Primero de todo, se define un marco para la optimización de trayectorias utilizado para generar las trayectorias simuladas para los experimentos definidos en esta tesis doctoral. A continuación, se evalúan los beneficios de volar CDOs de energía neutra comparándolas con trayectorias reales obtenidas de datos de vuelo históricos. Se comparan dos fuentes de datos, concluyendo cuál es la más adecuada para estudios de eficiencia en espacio aéreo terminal. Las CDOs de energía neutra son el tipo preferido de trayectorias desde un punto de vista medioambiental pero, dependiendo de la cantidad de tráfico, podría ser imposible para el ATC asignar una CTA que pueda ser cumplida por las aeronaves mientras vuelan la ruta de llegada publicada. En esta tesis doctoral, se comparan dos estrategias con el objetivo de cumplir con la CTA asignada: volar CDOs de energía neutra por rutas más largas/cortas o volar descensos con el motor accionado por la ruta publicada. Para ambas estrategias, se analiza la sensibilidad del consumo de combustible a diferentes parámetros, como la altitud inicial de crucero o la velocidad del viento. Finalmente, en esta tesis doctoral se analizan dos estrategias para gestionar de manera eficiente el tráfico de llegada en espacio aéreo terminal. Primero, se utiliza una estrategia provisional a medio camino entre la negociación completa de trayectorias 4D y la vectorización en “bucle abierto”: se propone una metodología para gestionar de manera eficaz tráfico de llegada donde las aeronaves vuelan CDOs de energía neutra en un procedimiento de navegación de área (RNAV) conocido como trombón. A continuación, se propone una nueva metodología para generar rutas de llegada dinámicas que se adaptan automáticamente a la demanda actual de tráfico. De igual manera, se aplican CDOs de energía neutra a todo el tráfico de llegada. Hay diferentes factores a considerar que podrían limitar los beneficios de las soluciones propuestas. La cantidad y distribución del tráfico de llegada tiene un gran efecto sobre los resultados obtenidos, limitando en algunos casos una gestión eficiente de las aeronaves de llegada. Además, algunas de las soluciones propuestas comportan elevadas cargas computacionales que podrían limitar su aplicación operacional, motivando mayor investigación en el futuro con el fin de optimizar los modelos y metodologías utilizados. Finalmente, permitir a algunos aviones volar descensos con el motor accionado podría facilitar la gestión de las aeronaves de llegada en los experimentos que se centran en el procedimiento de trombón y en la generación de rutas de llegada dinámicas.Postprint (published version

Towards large-scale and collaborative spectrum monitoring systems using IoT devices

Mención Internacional en el título de doctorThe Electromagnetic (EM) spectrum is well regulated by frequency assignment authorities, national regulatory agencies and the International Communication Union (ITU). Nowadays more and more devices such as mobile phones and Internet-of-Things (IoT) sensors make use of wireless communication. Additionally we need a more efficient use and a better understanding of the EM space to allocate and manage efficiently all communications. Governments and telecommunication operators perform spectrum measurements using expensive and bulky equipments scheduling very specific and limited spectrum campaigns. However, this approach does not scale as it can not allow to widely scan and analyze the spectrum 24/7 in real time due to the high cost of the large deployment. A pervasive deployment of spectrum sensors is required to solve this problem, allowing to monitor and analyze the EM radio waves in real time, across all possible frequencies, and physical locations. This thesis presents ElectroSense, a crowdsourcing and collaborative system that enables large scale deployments using Internet-of-Things (IoT) spectrum sensors to collect EM spectrum data which is analyzed in a big data infrastructure. The ElectroSense platform seeks a more efficient, safe and reliable real-time monitoring of the EM space by improving the accessibility and the democratization of spectrum data for the scientific community, stakeholders and the general public. In this work, we first present the ElectroSense architecture, and the design challenges that must be faced to attract a large community of users and all potential stakeholders. It is envisioned that a large number of sensors deployed in ElectroSense will be at affordable cost, supported by more powerful spectrum sensors when possible. Although low-cost Radio Frequency (RF) sensors have an acceptable performance for measuring the EM spectrum, they present several drawbacks (e.g. frequency range, Analog-to-Digital Converter (ADC), maximum sampling rate, etc.) that can negatively affect the quality of collected spectrum data as well as the applications of interest for the community. In order to counteract the above-mentioned limitations, we propose to exploit the fact that a dense network of spectrum sensors will be in range of the same transmitter(s). We envision to exploit this idea to enable smart collaborative algorithms among IoT RF sensors. In this thesis we identify the main research challenges to enable smart collaborative algorithms among low-cost RF sensors. We explore different crowdsourcing and collaborative scenarios where low-cost spectrum sensors work together in a distributed manner. First, we propose a fast and precise frequency offset estimation method for lowcost spectrum receivers that makes use of Long Term Evolution (LTE) signals broadcasted by the base stations. Second, we propose a novel, fast and precise Time-of-Arrival (ToA) estimation method for aircraft signals using low-cost IoT spectrum sensors that can achieve sub-nanosecond precision. Third, we propose a collaborative time division approach among sensors for sensing the spectrum in order to reduce the network uplink bandwidth for each spectrum sensor. By last, we present a methodology to enable the signal reconstruction in the backend. By multiplexing in frequency a certain number of non-coherent low-cost spectrum sensors, we are able to cover a signal bandwidth that would not otherwise be possible using a single receiver. At the time of writing we are the first looking at the problem of collaborative signal reconstruction and decoding using In-phase & Quadrature (I/Q) data received from low-cost RF sensors. Our results reported in this thesis and obtained from the experiments made in real scenarios, suggest that it is feasible to enable collaborative spectrum monitoring strategies and signal decoding using commodity hardware as RF sensing sensors.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Bozidar Radunovic.- Secretario: Paolo Casari.- Vocal: Fco. Javier Escribano Aparici

A Review of Environmental Context Detection for Navigation Based on Multiple Sensors

Current navigation systems use multi-sensor data to improve the localization accuracy, but often without certitude on the quality of those measurements in certain situations. The context detection will enable us to build an adaptive navigation system to improve the precision and the robustness of its localization solution by anticipating possible degradation in sensor signal quality (GNSS in urban canyons for instance or camera-based navigation in a non-textured environment). That is why context detection is considered the future of navigation systems. Thus, it is important firstly to define this concept of context for navigation and to find a way to extract it from available information. This paper overviews existing GNSS and on-board vision-based solutions of environmental context detection. This review shows that most of the state-of-the art research works focus on only one type of data. It confirms that the main perspective of this problem is to combine different indicators from multiple sensors

Accelerating Event Stream Processing in On- and Offline Systems

Due to a growing number of data producers and their ever-increasing data volume, the ability to ingest, analyze, and store potentially never-ending streams of data is a mission-critical task in today's data processing landscape. A widespread form of data streams are event streams, which consist of continuously arriving notifications about some real-world phenomena. For example, a temperature sensor naturally generates an event stream by periodically measuring the temperature and reporting it with measurement time in case of a substantial change to the previous measurement. In this thesis, we consider two kinds of event stream processing: online and offline. Online refers to processing events solely in main memory as soon as they arrive, while offline means processing event data previously persisted to non-volatile storage. Both modes are supported by widely used scale-out general-purpose stream processing engines (SPEs) like Apache Flink or Spark Streaming. However, such engines suffer from two significant deficiencies that severely limit their processing performance. First, for offline processing, they load the entire stream from non-volatile secondary storage and replay all data items into the associated online engine in order of their original arrival. While this naturally ensures unified query semantics for on- and offline processing, the costs for reading the entire stream from non-volatile storage quickly dominate the overall processing costs. Second, modern SPEs focus on scaling out computations across the nodes of a cluster, but use only a fraction of the available resources of individual nodes. This thesis tackles those problems with three different approaches. First, we present novel techniques for the offline processing of two important query types (windowed aggregation and sequential pattern matching). Our methods utilize well-understood indexing techniques to reduce the total amount of data to read from non-volatile storage. We show that this improves the overall query runtime significantly. In particular, this thesis develops the first index-based algorithms for pattern queries expressed with the Match_Recognize clause, a new and powerful language feature of SQL that has received little attention so far. Second, we show how to maximize resource utilization of single nodes by exploiting the capabilities of modern hardware. Therefore, we develop a prototypical shared-memory CPU-GPU-enabled event processing system. The system provides implementations of all major event processing operators (filtering, windowed aggregation, windowed join, and sequential pattern matching). Our experiments reveal that regarding resource utilization and processing throughput, such a hardware-enabled system is superior to hardware-agnostic general-purpose engines. Finally, we present TPStream, a new operator for pattern matching over temporal intervals. TPStream achieves low processing latency and, in contrast to sequential pattern matching, is easily parallelizable even for unpartitioned input streams. This results in maximized resource utilization, especially for modern CPUs with multiple cores