In order to facilitate collaborative decision-making in the Trajectory-Based Operation context, a strategic collaborative trajectory deconfliction method in Free Route Airspace is proposed. To achieve good trade-off between the objectives of safety, efficiency and equity, a multi-objective nonlinear integer model is formulated for the trajectory deconfliction problem by modifying the user-preferred trajectories with the combination of ground delay, rerouting and flight level allocation maneuvers. The Gini coefficient-based metric is introduced to quantify the inter-airline equity, and the airline priorities are incorporated in the model with the maximum allowed extra operation cost of each flight. Considering the large-scale and multi-objective characteristics of the problem, a decomposition-based memetic algorithm hybridized with local search operators is proposed. To balance the exploitation and exploration of the search process, A special hybridization scheme is employed for the setting of local search intensity and frequency. A traffic scenario in western China airspace with several free-routing sectors is used to validate the effectiveness of the proposed method. The results show that the proposed collaborative trajectory deconfliction method can provide high quality solutions to decision support in strategic trajectories planning.
Many overall safety factors need to be considered in the next generation of Urban Air Mobility (UAM) systems and addressing these can become the anchor point for such technology to reach consent for worldwide application. On the other hand, fulfilling the safety requirements from an exponential increase of prolific UAM systems, is extremely complicated, and requires careful consideration of a variety of issues. One of the key goals of these Unmanned Air Systems (UAS) is the requirement to support the launch and control of hundreds of thousands of these advanced drones in the air simultaneously. Given the impracticalities of training the corresponding number of expert pilots, achieving this goal can only be realized through safe operation in either fullautonomous or semi-autonomous modes. According to many recent studies, the majority of flight accidents are concentrated on the last three stages of a flight trip, which include the Initial Approach, Final Approach, and Landing Phases of an airplane trip. Therefore, this paper proposes a novel decentralized processing system for enhancing the safety factors during the critical phases of Vertical and/or Short Take-Off and Landing (V/STOL) drones. This has been achieved by adopting several processing and control algorithms such as an Open Fuzzy Logic System (FLS) integrated with a Flight Rules Unit (FRU), FIR filters, and a novel Prognostic Malfunction processing unit. After applying several optimization techniques, this novel coarse-grained Autonomous Landing Guidance Assistance System (ALGAS3) processing architecture has been optimized to achieve a maximum computational processing performance of 70.82 Giga Operations per Second (GOPS). Also, the proposed ALGAS3 system shows an ultra-low dynamic thermal power dissipation (I/O and core) of 145.4 mW which is ideal for mobile avionic systems using INTEL 5CGXFC9D6F27C7 FPGA chip.
This paper suggests a new authentication model using Online Certificate Status Protocol (OCSP) stapling with trusted responders to navigate a Public Key Infrastructure (PKI) trust tree in a constrained computing environment. This paper also suggests a model of how the trusted responders could be deployed and how to establish and maintain authentication between clients' applications and the trusted responder. Aircraft oftentimes work in limited RF bandwidth environments sharing a single frequency between many aircraft. Maximizing the efficiency of transmission time is paramount to servicing all aircraft communications needs. One of the large contributors to long transmission time in an IPS network is the login process, which requires the exchanging and verification of certificate chains. OCSP can be used to verify individual certificates, however it likely requires a request per certificate to the certificate authorities. Traditional OCSP responds with the validity information of a certificate, leaving the communicating counter parties, the constrained client and server, responsible for determining if the PKI tree between them is sufficiently strong to establish trust. The OCSP trusted responder model proposed by this paper would offload the PKI tree determination to trusted ground entities. The trusted ground entities would determine the level of trust, if any, for communication between the counter parties and forward only the necessary information for cryptographic exchange to the communicating counter parties. In an OCSP trusted responder model, a pre-configured list of trusted OCSP responders resides with the constrained client. During the authentication process the constrained client submits the list of OCSP trusted responders to the counter party server. The server is required to prove validity of its own PKI certificate using one of the OCSP trusted responders supplied by the client. The OCSP trusted responder would fetch any necessary intermediate certificates, walk the Public Key Infrastructure tree, and determine the level of trust, if any, between the counter parties and forward the information to the server. Thereby removing the need to exchange full certificate chains between constrained client and server. The server will then respond to the client with its own certificate, and the validation response from the OCSP trusted responder if any. The OCSP trusted responder model is adaptable and may be used in conjunction with, or in replace of, other verification methods of models.
The European Air Transportation Network was significantly impacted by the COVID-19 pandemic, resulting in an unprecedented loss of flight connections. Utilizing a combination of graph representation learning and time series analysis, this paper studies the evolution of both the global connectivity as well as the structure of the European Air Transportation Network from January 2020 to December 2022. Specifically, it finds strong differences in recovery rates for flights across six different market segments. In terms of network structure, the study finds that structural roles that are present in the pre-covid network have seen a loss in performance over the course of the pandemic, but have recovered to pre-covid levels. Using regional changes in structural roles, this study identifies Italy as the region with the strongest increase and the United Kingdom as the region with the strongest decrease in structural role, finding substantial differences in recovery rates per market segment. Lastly, this study pays special attention on the effect of the Russia-Ukrainian war on the European Air Transportation Network.
Aviation performance depends on education and training of pilots and air traffic controllers (ATCOs) on technical, procedural, crisis management, decision making, leadership and communication skills. Recent incidents and studies, however, show crew members may be caught unaware and errors in human judgement can still occur in air traffic control (ATC) systems even more so in the presence of cyberattacks. This paper focuses on raising situation awareness and decision making of ATCOs with cyberattacks impacting operations in next-generation ATC systems. Our goal is to investigate crew-based cyber security as a layer in making ATC systems resilient to cyber and cyber- physical attacks. Automatic Dependent Surveillance Broadcast (ADS-B) technology is used as the basis of our preliminary investigation due to its widely known security concerns and central role in ATC systems. We present experimental study considerations for assessing cyber readiness and improving training of ATCOs. We propose a Kalman filter based cyber alarm solution approach for a machine learning based aid for ATCOs towards early cyberattack detection and incident response in ATC systems.
This paper presents results of real flight evaluation of integrated communication, navigation and surveillance solution equipment for both manned and unmanned aerial vehicles aiming to share the same airspace. An equipment set, consisting of an experimental CNS device connected to Internet over an LTE modem with a proper antenna, and own battery is developed and installed on an aircraft, a rotorcraft and two drones. The usability of this equipment is evaluated from both technical and operational point of view in the UTM and ATM reflexes exhibited in the cases of area violation and route deviation of manned / unmanned aerial vehicles in controlled and uncontrolled airspace.
In this paper we aim at introducing Unmanned Aerial Vehicles (UAVs) to prevent accidents, considering a use case that is likely to happen in cities where vehicles are circulating and when approaching an intersection like a pedestrian crossing, a railway crossing or a crossroad. A first UAV observes from the top of the scene and will, in certain cases, notify a second UAV which will exchange information either with the car driver if its behavior is far from a reference profile, or with the infrastructure in the ultimate case. We highlight interactions between the two UAVs (U2U communication) as well as between the UAV and the car vehicle (U2V communication) and between the UAV and infrastructure (U2I communication). Hence, we emphasize the benefit of using UAVs to enhance safety and reduce accident gravity rate.
This paper explores the capability of a ground-based radar network deployed in urban environment for providing accurate surveillance information to support traffic deconfliction and detect and avoid operations. It proposes a simulation environment modelling a network of radars impacted by different weather condition and urban clutter. The capability of the radar network in providing precise aircraft tracks is evaluated by simulation considering flight test scenarios properly adapted from DO-381.
This study presents an approach for pre-flight replanning process to be used in the future Advanced Air Mobility (AAM) system especially after contingency situations and relevant activities take place. The methodology for pre-flight replanning phase is analyzed and modeled in two steps as optimization based potential conflict resolution and demand capacity balancing, which respectively provides safety for the surrounding traffic and efficiency for the traffic network in case of a contingency. These two models can work iteratively to achieve pre-flight replanning for the Unmanned Aircraft System Traffic Management (UTM). The developed pre-flight replanning model can also be used at strategic planning phase. For the use cases, a very large UTM traffic network is considered to have a highly dense traffic environment since the expected complexity is high with the AAM system and to show the efficiency and scalability of the models. Two use cases are examined. First one is about initial flight planning where the conflicted flight plans are safely separated and balance in demand and capacity at vertiports is provided. Second scenario is related to potential conflict resolution for the flights at pre-tactical phase after contingency events observed within the network and demand-capacity balancing after safety related events are resolved. The main objective of this work is to develop a pre-flight replanning service to work compatible with contingency management activities to build the introduced system-wide contingency management concept for the AAM system.
Unmanned aerial vehicles (UAVs) have emerged as a promising platform for various applications, including inspection, surveillance, delivery, and mapping. However, one of the significant challenges in enabling UAVs to perform these tasks is the ability to navigate in indoor environments. Visual navigation, which uses visual information from cameras and other sensors to localize and navigate the UAV, has received considerable attention in recent years. In this paper, we propose a new approach for visual navigation of UAVs in indoor corridor environments using a monocular camera. The approach relies on a novel convolutional neural network (CNN) called Res-Dense-Net, which is based on the ResNet and DenseNet networks. Res-Dense-Net analyzes the images captured by the UAV’s camera and predicts the position and orientation of the UAV relative to the environment. To demonstrate the effectiveness of the proposed approach, experiments were conducted on the NitrUAVCorridorV1 dataset. The proposed approach achieves high accuracy in estimating the position and orientation of the UAV, even in challenging environments with limited visual cues and provides high real-time performance based on visual data from a monocular camera, which can significantly enhance the capabilities of UAVs for various applications.
In the course of the European project CORUS-XUAM, a very large-scale demonstration was conducted for the metropolitan area of Frankfurt, for which a newly developed U-space route structure was conceptually elaborated. The demonstration exercise focuses on the initial definition and a near-term implementation of urban U-space corridors inside controlled airspace (class D) connecting terminal 2 of the Frankfurt Airport and the Frankfurt Trade Fair via air taxi services operating as airport shuttles. With advancements in navigation performance, monitoring, surveillance, communication technology and increasing operating experience, U-space corridors can transition to free flight trajectories. Local constraints such as approaching fixed-wing traffic, flights operating under visual flight rules (VFR) including helicopter emergency medical services, heliport operations, and a VFR holding pattern were taken into account in order to elaborate the course of the U-space corridors. Of special interest is the harmonization of airspace users inside and outside of U-space while ensuring safe and efficient conflict detection and resolution. A set of fast-time simulations have been conducted to evaluate the developed U-space route structure and its operating concept based on historic air traffic data for the metropolitan area of Frankfurt. Following the hypotheses that every 120 seconds an air taxi departure can be performed without causing a negative impact on airport operations, we evaluate the metrics U-space corridor usage, occupancy, throughput, number of resolved conflicts and occurring/imposed delay. In addition, we elaborate where conflicts occur and whether another air taxi or the background traffic caused them. Furthermore, we show how they can be resolved, i.e., how often and to what extend delaying and/or re-routing is applied throughout different scenarios. Using the selected separation values, it is possible to have air taxis departing every 90 s at both vertiports. This certainly changes when different separation thresholds are used. Additionally, we showed that all conflicts can be solved by applying a small ground delay or by simply choosing a different U-space corridor.
With the growth of unmanned aviation, there has been an increased interest, or rather need, to define a framework or ecosystem that enables safe and efficient access to airspace for a large number of Unmanned Aircraft Systems (UAS), what is known as UAS Traffic Management (UTM). The development of UTM identifies services, roles, and responsibilities, as well as information exchanges between stakeholders. Among the challenges identified by the UTM, conflict management and separation provision stand out. In this context, BUBBLES, an Exploratory Research (ER) project funded by SESAR Joint Undertaking (SJU), defines a new concept of operations (ConOps) for the separation management service, a UTM service dealing with conflict management in tactical phase. The present paper outlines this ConOps, defines the architecture of a UTM tool developed to validate the BUBBLES concept, named Separation Management Environment (SME), and presents the validation exercise performed by means of test flights.
Low-cost Unmanned Aerial Vehicles (UAVs) and their swarms are increasingly being used and have become an integral part of various industries and aspects of daily life. They are being used in industries such as agriculture, delivery services, construction and to achieve critical tasks such as disaster relief, search and rescue operations, etc. As Unmanned Aerial Vehicles (UAVs) become increasingly pervasive and essential, they are becoming both a target for attack and the very weapon on which such attacks are carried out. For example, UAVs can be hacked and used to perform cyber attacks on targets such as power grids, and communication and military networks. To protect UAVs against cyber-attacks, different techniques both traditional (cryptography-based) and non-traditional (sound-based, video-based, physical layer (PHY) based) have been proposed in the literature. Traditional crypto-based authentication solutions usually involve high computational overhead and require the challenging task of managing secret keys. Recently introduced PHY-based solutions that exploit the inherent randomness of the wireless channels and non-ideal hardware characteristics have witnessed an increased interest from the research community resulting in many publications. Nevertheless, we noticed a lack of an up-to-date survey that keeps track of all the recent research developments in this new area of security research. Thus, the goal of this paper is to bridge the gap in the literature by providing a timely and comprehensive discussion on different techniques and methods proposed in the literature, summarizing their working principles, and highlighting the various strengths and weaknesses. We also discuss different challenges that physical layer security faces and potential future opportunities.
The objective of this study is to evaluate benefits of the ground-based augmentation system (GBAS) at large U.S. airports that also use the traditional instrument landing system (ILS). Landing operations are analyzed at GBAS-equipped and non-GBAS-equipped airports and airlines to determine if there is any significant difference in their performance. After comparing performance of airports (GBAS- and non-GBAS-equipped) and airlines (one GBAS- and non-GBAS-equipped), we draw a comparison matrix for the following flight performance metrics: block delay, Expect Departure Clearance Times (EDCT) arrival delay, and gate arrival delay. The analysis results reveal that arrival delays are lower at GBAS-airports, while GBAS equipage onboard aircraft does not show any significance. This might imply that airports benefit more from GBAS than airlines. Considering the fact that airports and private entities are responsible for major expenses of GBAS airport equipment, these study findings can be used in cost-benefit analyses when considering GBAS purchasing. Since an ILS system consists of many components located around a runway, this study also evaluates the reliability and availability of ILS systems across Tier 1 U.S. airports; and it compares their performance at GBAS- and non-GBAS-equipped airports using the following metrics: mean time between outages (MTBO), and outage downtime. The analysis results reveal the following: (1) at GBAS-equipped airports, ILS components have longer MTBO when compared to non-GBAS airports, implying that these components fail less frequently; and (2) glide slope (GS) and runway visual range (RVR) outage downtimes at GBAS airports are shorter than at non-GBAS airports, while Localizer (LOC) downtimes at GBAS airports are longer. Since LOCs are mandatory for precision and non-precision approaches, the fact that LOC downtimes are longer at GBAS airports than at non-GBAS airports might imply that because GBAS airports can utilize CAT I precision approaches without using LOCs, LOC failures don’t have to be repaired as quickly as at non-GBAS airports.
In this paper, we discuss an analysis method to describe the human and automation roles in increasingly autonomous aviation systems. Specifically, we discuss the relationship between humans and automation in the performance of individual functions in two dimensions, related to their responsibility and authority for the control loops associated with an individual intended function. This work builds upon concepts and terminology proposed by ASTM International in Autonomy Design and Operations in Aviation: Terminology and Requirements Framework (TR1) published in 2019 and is based in part on the on-going work of an ASTM Working Group (WK76044) under F39 Aircraft Systems. TR1 presented a Contextual Framework for analysis of automated aviation functions, and the working group is developing a practice for its implementation.The role of the human vs. the role of automation, which has been extensively discussed in the past, is fundamental to the implementation of the Contextual Framework. As such, it is worth a fresh look, particularly against the backdrop of the Contextual Framework. Our work has led to an analysis of the system state, which at any given time is defined by the role of the human and the role of automation in a two-dimensional space. We refer to this as Dimensional Role Analysis which uses a directed graph approach to clearly depict the nominal operating mode, the revisionary modes, and the triggering events that would cause transition. The analysis approach helps to establish clear delineations as to the responsibility and authority of humans and automation in the performance of individual intended functions. This work makes a clear distinction between authority, responsibility, and accountability consistent with the work of ASTM.Using the concepts and language presented in this paper could add clarity to discussions in the aviation community, as proponents seek to certify and obtain operational approval for increasingly autonomous systems in aviation. While this paper focuses on aircraft control, these approaches could also apply to other safety critical systems.
Achieving Advanced Air Mobility (AAM) on a scale envisioned by industry proponents and other stakeholders will require an Air-Ground Communication (AG Comm) system that is robust and resilient to failures. In this paper we describe a Machine Learning-based tool that quickly predicts communication path loss for AAM flights, a key metric for establishing and maintaining robust AG Comm. We have implemented this tool and tested it using both simulated scenarios and live flight data. This paper describes the tool itself and the results obtained comparing it with "ground truth" as established through physics-based ray-tracing computations.
Following an increased interest in aviation decarbonization, electric aircraft - including conventional fixed-wing and electric Vertical Take-Off & Landing aircraft (eVTOLs) - are approaching certification. While several studies and eVTOL manufacturers claim noise pollution reduction near airports to be a significant benefit of replacing existing aircraft with electric aircraft, there is little research quantifying this suggested benefit. This study develops a method for quantifying noise footprint benefits of electric aviation at regional airports. The method is applied to Van Nuys airport (VNY) in the Los Angeles Basin by simulating noise levels in a 7-mile radius around the airport and measuring the difference in total area affected by high noise levels (> 55dB as set by the FAA) for three scenarios: the base case, 20% electric aircraft, and 50% electric aircraft. The proposed method can be applied to other regional airports to compute their noise footprint. Understanding the noise footprint of airport operations is critical for land use zoning of the surrounding area and hence, sustainable development of community infrastructure and regional air mobility. Such noise simulations can also be used to guide the development of novel methods of urban and regional air mobility. In both the 20% and 50% electrification scenarios, the VNY noise analysis results show the following: (a) the number of highly annoyed individuals decreased by 7.42% and 14.91% respectively; (b) the areas with high Day-Night Level (DNL) are reduced; (c) the percentage of noise reduction is larger in areas with high base case DNL above 65 dB; and (d) the presence of a large disparity in DNL between Disadvantaged Communities (DACs) and non-DACs in five neighborhoods.
The operation of Uncrewed Aircraft Systems (UAS) at airports is becoming more common. The increased use of small and inexpensive drones pose various challenges associated with integrating UAS operations in the national airspace system. For non-military applications, Uncrewed Aircraft (UA)s are allowed to fly below 400ft which helps to segregate UAS operations from crewed aircraft. This however does not negate the challenges and additional risk in low altitude scenarios such as in and around airports when UASs are used for commercial and security applications. Flying the UASs in such controlled airspace needs permission from authorities such as Air Traffic Control (ATC) and other controlling agencies. For such scenarios where UAS operations need to be integrated along with controlled airspace, it is important to understand and estimate the associated risk. An unintentional malfunction resulting in uncontrolled UASs poses multiple risks, particularly when operated in a busy airport environment. This includes infrastructure, ground, and air risk. When left unmitigated, such scenarios will lead to the disruption of regular operations and cause loss to the economy and sometimes human life. The paper focuses on developing an assessment tool for UAS collision risk with crewed aircraft in an airport scenario. The study focuses on the UAS risk associated with crewed aircraft flying below 1000 ft altitude within a 5 mile radius of an airport. High volume airport operations combined with low-altitude flights results in an increased risk of collision within an airport environment. The trajectory of aircraft in three-dimensional space is determined using actual historical Automatic Dependent Surveillance-Broadcast (ADS-B) data. Simulations are conducted to model various fail-safe scenarios of UASs. A probabilistic approach is used to model UA paths that assign Gaussian distributions to the mean values of the UAS’s velocity, heading, and altitude. The framework developed can be further expanded to include specific waypoints or routes other than ADS-B data. The study aims to calculate the probabilities of Mid-Air Collisions (MAC), Near Mid-Air Collisions (NMAC), and Well Clear (WC) violations entering these protected volumes between uncrewed and crewed aircraft. Initially, the study modeled the Grand Forks International Airport, which has a high volume of airport operations. Historical ADS-B data is analyzed statistically to identify the highest volume of traffic for a given day. Flight trajectories from that time interval are then extracted for analysis. The UAS-flight risk assessments are carried out for various scenarios that include the velocity of the aircraft, traffic volume, and the probability distributions of the UAS’s trajectory.
Traditionally, the transportation system’s resiliency to the impacts of weather is an area where neglected or incorrect assumptions can lead to difficulties later in the research and development lifecycle. To mitigate this, NASA has ongoing efforts to develop a set of research roadmaps for organizing, integrating, and communicating research into new aviation infrastructure and transportation modalities, within which weather is being addressed early on. An effort has been undertaken to add weather assumptions and requirements to an already-existing roadmap for the Urban Air Mobility (UAM) airspace, seeking to integrate weather requirements early in the system design. This effort addresses the way in which state-of-the art and evolving weather science and technology can enable safe and efficient travel with increasing tempo of UAM operations over time. This paper describes the addition of weather as one of 10 capabilities into the UAM Airspace research roadmap, laying out the anticipated weather technology and information requirements needed to facilitate operations at various UAM Maturity Levels. The process developed and exercised by MIT Lincoln Laboratory researchers produced 41 unique requirements to be satisfied by a Weather capability for the UAM ecosystem, with more than 300 dependencies identified across the system. These requirements cover measurement, analysis, modeling, forecasting, decision support, dissemination, and overarching policy, and are provided with an overview of weather challenges for UAM. The requirements were mainly defined based on subject matter expert review of existing UAM Airspace system requirements, and refined based on iterative feedback with various stakeholders including regulators, academia, and industry. Going forward, this roadmap will help researchers and developers align to a common vision in ensuring that weather is appropriately considered in the UAM ecosystem.
Faced with climate change and global warming, the reduction of greenhouse gas emissions, such as carbon dioxide, is a modern day focus of politics, industry and society. The aviation industry, responsible for about 3.5% of worldwide greenhouse gas emissions, is currently heavily depending on fossil fuels. As such, efforts are made to move towards a sustainable green aviation and carbon neutrality. Projects undertaken by authorities, airlines or manufacturers vary from new technologies like electric propulsion or Sustainable Aviation Fuel (SAF), to adjustments of operating procedures such as the retrieval of wake energy. [22] One already tested Wake Energy Retrieval (WER) technology is Airbus' fello'fly project. Hereby, different aircraft are positioned closely together on intercontinental flights, such that the succeeding aircraft can benefit from upward air movement within the wake created by the preceding one. Flight trials conducted in 2021 have shown a significant opportunity to reduce fuel consumption and therefore the carbon dioxide emissions and costs on long-range flights. However, to benefit from wake energy requires a reduction in currently established safety distances between aircraft. As introduction into service is envisioned as early as 2025, the fello'fly system will be based on already existing technologies. This paper focuses on possible safety and security implications created by this combination of reduced safety distances and reliance on legacy communication, navigation and surveillance technologies. First, regulations governing aircraft separation will be introduced, followed by an introduction to the concept of fello'fly. Furthermore, the paper offers an overview of the employed technologies and underscores their known cybersecurity vulnerabilities. The implications of using these systems alongside reduced separation in an evolving threat landscape are examined. The paper concludes by proposing necessary enhancements for a secure implementation of wake energy retrieval.
