Airspace Environment Research Articles

Realizing a 21st Century Noise Policy

Aviation noise impacts affect the health and quality of life of communities nationwide. The FAA's noise policy, last updated in the 1970s, uses a single decision-making metric (DNL), to determine the significance of noise impacts caused by aircraft operations. The Neighborhood Environmental Survey (NES), released in 2021, shows that many more people are impacted by aircraft noise and at levels far below 65 dB DNL than previously thought. The current noise policy does not reflect the 21st century airspace environment, including the consequences of NextGen and the tremendous growth in air traffic. An important improvement to realize an up-to-date noise policy is to reflect the lived experience of impacted communities more accurately. This paper will cover how communities experience aircraft noise in two separate noise exposure environments both near and away from airports, which practices are issues and most concerning to communities, what metrics more accurately evaluate impacts, and how a more community-centric approach provides a better representation of the aviation noise experienced by people, which is fundamental to operating a National Airspace System that works for all.

Aircraft Categorization Approach Using Machine Learning to Analyze Aircraft Behavior

The establishment of aircraft categories is a classification technique employed in a variety of aviation disciplines, including design and development, certification, ongoing airworthiness, air traffic management, surveillance, and safety analysis. Traditional approaches rely on manual feature engineering, which can be labor-intensive and ineffective for capturing complex patterns. In this paper, an approach to aircraft categorization using unsupervised machine learning clustering is proposed. The aim of the proposed approach is to be simple in order to be useful and understandable across disciplinary domains; to be scalable to large volumes of air traffic data; and to be adaptable to changes to account for the evolving technological and operational nature of the airspace environment. The application is based on an adapted version of the k-means algorithm that can group aircraft into clusters based on 3D position over time. The approach is validated using real-world, publicly available ADS-B air traffic data, and the results are compared to traditional categorization methods from the field of aircraft certification. The results showed that the model could be used to 1) identify and group aircraft sharing the same flight phase, 2) categorize aircraft with a similar general heading or direction, and 3) distinguish between local regional aircraft operations and longer flight operations. It was also shown that, depending on the use case, the model could be extended to identify more granular behaviors by increasing the K value used to create the model. Overall, the findings demonstrate that leveraging machine learning techniques for aircraft categorization provides an effective, automated, and scalable solution applicable to a wide range of current applications.

Nanoparticle targeting of neutrophil glycolysis prevents lung ischemia-reperfusion injury

Neutrophils exacerbate pulmonary ischemia-reperfusion injury (IRI) resulting in poor short and long-term outcomes for lung transplant recipients. Glycolysis powers neutrophil activation, but it remains unclear if neutrophil-specific targeting of this pathway will inhibit IRI. Lipid nanoparticles containing the glycolysis flux inhibitor 2-deoxyglucose (2-DG) were conjugated to neutrophil-specific Ly6G antibodies (NP-Ly6G[2-DG]). Intravenously administered NP-Ly6G(2-DG) to mice exhibited high specificity for circulating neutrophils. NP-Ly6G(2-DG)–treated neutrophils were unable to adapt to hypoglycemic conditions of the lung airspace environment as evident by the loss of demand-induced glycolysis, reductions in glycogen and ATP content, and an increased vulnerability to apoptosis. NP-Ly6G(2-DG) treatment inhibited pulmonary IRI following hilar occlusion and orthotopic lung transplantation. IRI protection was associated with less airspace neutrophil extracellular trap generation, reduced intragraft neutrophilia, and enhanced alveolar macrophage efferocytotic clearance of neutrophils. Collectively, our data show that pharmacologically targeting glycolysis in neutrophils inhibits their activation and survival leading to reduced pulmonary IRI.

Poster] Spatial modelling of midair collision risk using ADS-B data


 The airspace environment is a complex system that is only expected to continue increasing in complexity with the introduction of new types of air vehicles and operations, such as uncrewed aircraft, and the projected growth of air traffic volumes. This increase in complexity brings a need for investigating and developing new models of complex airspace environments to better understand their constituent parts. To address this need, this paper presents a methodology to create a geospatial model of complex airspace environments which can be used to study any geospatially distributed entity part of these environments. The methodology leverages Discrete Global Grid Systems (DGGS) for this purpose, a Geographic Information Systems framework previously utilized in geography and urban planning. The usefulness of the model is demonstrated on a case study geospatial entity which is the risk of midair collisions for many geospatially distributed points in an airspace region of interest. Since such a model needs to be able to work for any type of air vehicle and airspace region in a fully three-dimensional model that is capable of performing time varying analysis in a computationally efficient manner, a rudimentary midair collision risk model was also developed which satisfies these requirements. Air traffic data from the OpenSky Network was collected and integrated in the geospatial model and the midair collision risk model was used to calculate the risk of collisions for many geospatially distributed points in the airspace for four scenarios of ascending airspace complexity. The results from these four scenarios showed that the proposed methodology can be used to study the risk of midair collisions for different points in the airspace for any type of air vehicle and airspace region of interest in a fully three-dimensional model that is capable of performing time varying analysis in a computationally efficient manner.
 

An Intelligent UAV Path-Planning Method Based on the Theory of the Three-Dimensional Subdivision of Earth Space

With the rapid development of the big data era, Unmanned Aerial Vehicles (UAVs) are being increasingly adopted for various complex environments. This has imposed new requirements for UAV path planning. How to efficiently organize, manage, and express all kinds of data in complex scenes and intelligently carry out fast and efficient path planning for UAVs are new challenges brought about by UAV application requirements. However, traditional path-planning methods lack the ability to effectively integrate and organize multivariate data in dynamic and complicated airspace environments. To address these challenges, this paper leverages the theory of the three-dimensional subdivision of earth space and proposes a novel environment-modeling approach based on airspace grids. In this approach, we carried out the grid-based modeling and storage of the UAV flight airspace environment and built a stable and intelligent deep-reinforcement-learning grid model to solve the problem of the passage cost of UAV path planning in the real world. Finally, we designed multiple sets of experiments to verify the efficiency of the global subdivision coding system as an environmental organization framework for path planning compared to a longitude–latitude system and to demonstrate the superiority of the improved deep-reinforcement-learning model in specific scenarios.

Preliminary Concept of Urban Air Mobility Traffic Rules

Driven by recent technological breakthroughs, the electric vertical take-off and landing (eVTOL) aircraft has gained considerable attention. The widespread demand for eVTOL aircraft can be attributed to their potential use in the commercialisation of urban air mobility (UAM) in low-altitude urban airspaces. However, the urban low-altitude airspace environment is complex. UAM has a high traffic density and the eVTOL aircraft specifications are not uniform. Particularly in commercial scenarios, controlling eVTOL aircraft and ensuring safety in UAMs are the two major problems that should be addressed in future studies. The design of reasonable traffic rules is a potential solution; hence, we organised a UAM traffic rule system and proposed several alternative UAM traffic rules from three perspectives: a single eVTOL aircraft, a certain route, and key control areas. In addition, we validated these traffic rules using multi-rotor and fixed-wing eVTOL aircraft. The results show that designing reasonable traffic rules can facilitate attaining the primary objectives of commercialisation of UAM.

Safety Evaluation Method and Management Strategy for Aviation Flight Plans

Aviation resources in the post-pandemic era are still in short supply. The increasing air traffic flow aggravates flight delays and makes it difficult to ensure aviation safety. Instead of focusing on the economic benefits, this paper proposes a quantitative method for evaluating the safety of flight plans. A flight plan safety index system is constructed through airspace environment modelling and a conflict probability calculation. The proposed method provides a quantitative basis for the management and adjustment of flight plans at the strategic level. Improving the flight plan management strategy from the perspective of conflict avoidance is expected to fundamentally reduce the potential conflict and workload of controllers and pilots during flight and improve the safety level of the whole air transport system. Finally, the performance of the proposed flight plan safety evaluation method is demonstrated through an illustrative air traffic scenario.

Scalable Autonomous Separation Assurance With Heterogeneous Multi-Agent Reinforcement Learning

In this article, a scalable autonomous separation assurance framework is proposed for high-density en route airspace sectors with heterogeneous aircraft objectives. To handle the complex dynamic decision making under uncertainty, multi-agent reinforcement learning is used in a decentralized approach with each aircraft being represented as an agent. Based on this, each agent locally solves the separation assurance problem, allowing the framework to scale to a large number of aircraft. In addition, each agent has the ability to learn the intention of the intruder aircraft, which is essential in environments with heterogeneous agents. Numerical experiments are performed in a real-time air traffic simulator. The results demonstrate that the proposed framework is able to effectively ensure the safe separation of heterogeneous agents, while also optimizing the intrinsic agent objectives in high-density en route airspace sectors. In addition, the efficiency of the proposed framework is demonstrated and shown to provide real-time decision making for separation assurance. Note to Practitioners—In commercial aviation, the workload of human air traffic controllers increases with the growth of air traffic density. Robust decision making systems to augment human air traffic controllers allows for increased air traffic without increased workload, resulting in a safer airspace environment. In addition, advanced air mobility (AAM) is concerned with low-altitude airspace operations with both human and autonomous pilots. In this environment, autonomous real-time separation assurance systems are required. Most traditional separation assurance approaches fail to handle stochastic and high-density environments, rendering them inapplicable to future high-density traditional airspace and the envisioned low-altitude AAM operations. Therefore, it is important to study decentralized approaches that can place the separation assurance problem as an intrinsic objective of each aircraft to ensure cooperation in high-density airspace. With this consideration, a scalable, decentralized autonomous separation assurance framework capable of handling heterogeneous agents is proposed in this article. This framework is able to perceive the current air traffic environment and select speed advisories to ensure safe separation requirements, while balancing intrinsic objectives such as minimizing delay. While one limitation of multi-agent reinforcement learning is the long training time, this article demonstrates how the framework also can leverage modern computing clusters to significantly reduce training time without sacrificing performance.

Three-Dimensional Path Planning for Unmanned Helicopter Using Memory-Enhanced Dueling Deep Q Network

Unmanned helicopter (UH) is often utilized for raid missions because it can evade radar detection by flying at ultra-low altitudes. Path planning is the key technology to realizing the autonomous action of UH. On the one hand, the dynamically changing radar coverage area and the mountains in the low airspace environment will seriously affect the flight safety of UH. On the other hand, the huge state space of the three-dimensional (3D) environment will also make traditional algorithms difficult to converge. To address the above problems, a memory-enhanced dueling deep Q-network (ME-dueling DQN) algorithm was proposed. First, a comprehensive reward function was designed, which can guide the algorithm to converge quickly and effectively improve the sparse reward problem. Then, we introduced a dual memory pool structure and proposed a memory-enhanced mechanism, which can reduce invalid exploration, further improve the learning efficiency of the algorithm, and make the algorithm more stable. Finally, the path planning ability of the proposed algorithm in multiple experimental environments was verified. Experiments showed that the proposed algorithm has good environmental adaptability and can help UH to accurately identify dangerous areas and plan a safe and reliable flight path.

DUG Insight: A software package for big-data analysis and visualisation, and its demonstration for passive radar space situational awareness using radio telescopes

As the demand for software to support the processing and analysis of massive radio astronomy datasets increases in the era of the SKA, we demonstrate the interactive workflow building, data mining, processing, and visualisation capabilities of DUG Insight. We test the performance and flexibility of DUG Insight by processing almost 68,000 full sky radio images produced from the Engineering Development Array (EDA2) over the course of a three day period. The goal of the processing was to passively detect and identify known Resident Space Objects (RSOs: satellites and debris in orbit) and investigate how radio interferometry could be used to passively monitor aircraft traffic. These signals are observable due to both terrestrial FM radio signals reflected back to Earth and out-of-band transmission from RSOs. This surveillance of the low Earth orbit and airspace environment is useful as a contribution to space situational awareness and aircraft tracking technology. From the observations, we made 40 detections of 19 unique RSOs within a range of 1,500 km from the EDA2. This is a significant improvement on a previously published study of the same dataset and showcases the flexible features of DUG Insight that allow the processing of complex datasets at scale. Future enhancements of our DUG Insight workflow will aim to realise real-time acquisition, detect unknown RSOs, and continue to process data from SKA-relevant facilities.

Orbital collaborative learning in 6G space-air-ground integrated networks

Currently, 6G is being proposed as a new next-generation communication architecture. The inclusion of satellite communication networks makes it possible to genuinely space-air-ground integrated networks, especially the combination of 6G with machine learning algorithms, which has attracted increasing attention. Of particular note is the combination of collaborative learning with the Internet of Everything, which improves the intelligence and collaboration of devices with more robust privacy protection, higher computing power, and greater energy savings. However, the airspace environment in 6G is extraordinarily dynamic and delay-sensitive for services, and traditional federation learning is a synchronous aggregation model with slow convergence. To address this problem, the paper proposes a hierarchical satellite-ground collaborative architecture with three roles: remote cloud center, orbital edge computing server, and data node. We use the advantages of orbital edge computing and low-orbit satellite network communication to manage the parameter transfer problem during the collaboration process. Meanwhile, we propose an asynchronous adaptive collaborative aggregation algorithm (AFLS) for the satellite-ground architecture, enabling the federated members to update the model parameters in time during the dynamic joining aggregation process. Experiments show that our architecture and algorithm excel in training performance with 95%accuracy and fewer communication rounds in image classification when the roles are in good collaboration.

Impact of Spatial Orientation Ability on Air Traffic Conflict Detection in a Simulated Free Route Airspace Environment.

In the selection of job candidates who have the mental ability to become professional ATCOs, psychometric testing has been a ubiquitous activity in the ATM domain. To contribute to psychometric research in the ATM domain, we investigated the extent to which spatial orientation ability (SOA), as conceptualized in the spatial cognition and navigation literature, predicted air traffic conflict detection performance in a simulated free route airspace (FRA) environment. The implementation of free route airspace (FRA) over the past few years, notably in Europe, have facilitated air traffic services by giving greater flexibility to aviation operators in planning and choosing preferred air routes that can lead to quicker arrivals. FRA offers enhanced system safety and efficiency, but these benefits can be outweighed by the introduction of air traffic conflicts that are geometrically more complex. Such conflicts can arise from increased number and distribution of conflict points, as well as from elevated uncertainty in aircraft maneuvering (for instance, during heading changes). Overall, these issues will make conflict detection more challenging for air traffic controllers (ATCOs). Consequently, there is a need to select ATCOs with suitably high levels of spatial orientation ability (SOA) to ensure flight safety under FRA implementation. In this study, we tested 20 participants who are eligible for ATCO job application, and found that response time-based performance on a newly developed, open access, computerized spatial orientation test (SOT) predicted time to loss of minimum separation (tLMS) performance on an air traffic conflict detection task (AT-CDT) we designed. We found this predictive relationship to be significant to a moderately large extent under scenarios with high air traffic density (raw regression coefficient = 0.58). Moreover, we demonstrated our AT-CDT as a valid test in terms of eliciting well-known mental workload and spatial learning effects. We explained these findings in light of similar or overlapping mental processes that were most likely activated optimally under task conditions featuring approximately equal numbers of outcome-relevant stimuli. We conclude by discussing the further application of the SOT to the selection of prospective ATCOs who can demonstrate high levels of conflict detection performance in FRA during training simulations.

A low-altitude public air route network for UAV management constructed by global subdivision grids.

With an increasing number of unmanned aerial vehicles (UAVs), the difficulty of UAV management becomes more challenging, especially for low-altitude airspace due to complicated issues of security, privacy and flexibility. Existing management approaches to UAV flights include implementing registration of flight activity for supervision purposes, limiting the maximum flight height, setting different zones for different flight activities and prohibiting flights. In this research, we proposed a new air traffic management method for UAVs based on global subdivision theory. We designed four types of low-altitude air routes from grids, which correspond to grid sizes of 1.85 km, 128 m, 64 m and 32 m. Utilization of the subdivision grids transforms the complex spatial computation problem into a query process in the spatial database, which provides a new approach to UAV management in the fifth-generation (5G) era. We compared the number and data size of stored track records using longitude and latitude and different grid levels, computed time consumption for air route trafficability and simulated UAV flight to verify the feasibility of constructing this type of air traffic highway system. The amount of data storage and time consumption for air route trafficability can be substantially reduced by subdivision. For example, the data size using traditional expressions of latitude and longitude is approximately 1.5 times that of using a 21-level grid, and the time consumption by coordinates is approximately 1.5 times that of subdivision grids at level 21. The results of the simulated experiments indicate that in the 5G environment, gridded airspace can effectively improve the efficiency of UAV trajectory planning and reduce the size of information storage in the airspace environment. Therefore, given the increasing number of UAVs in the future, gridded highways have the potential to provide a foundation for various UAV applications.

Study on the resolution of multi-aircraft flight conflicts based on an IDQN

With the rapid growth of flight flow, the workload of controllers is increasing daily, and handling flight conflicts is the main workload. Therefore, it is necessary to provide more efficient conflict resolution decision-making support for controllers. Due to the limitations of existing methods, they have not been widely used. In this paper, a Deep Reinforcement Learning (DRL) algorithm is proposed to resolve multi-aircraft flight conflict with high solving efficiency. First, the characteristics of multi-aircraft flight conflict problem are analyzed and the problem is modeled based on Markov decision process. Thus, the Independent Deep Q Network (IDQN) algorithm is used to solve the model. Simultaneously, a ‘downward-compatible’ framework that supports dynamic expansion of the number of conflicting aircraft is designed. The model ultimately shows convergence through adequate training. Finally, the test conflict scenarios and indicators were used to verify the validity. In 700 scenarios, 85.71% of conflicts were successfully resolved, and 71.51% of aircraft can reach destinations within 150 s around original arrival times. By contrast, conflict resolution algorithm based on DRL has great advantages in solution speed. The method proposed offers the possibility of decision-making support for controllers and reduce workload of controllers in future high-density airspace environment.

Adaptive Separation Thresholds for Self-Separation of Unmanned Aircraft System in Dynamic Airspace

The key challenge to integrate the unmanned aircraft system (UAS) into airspace is to develop a means to sense and avoid (SAA) other aircrafts. The main function of the SAA is self-separation, i.e. remaining “well clear” of the other aircrafts. The separation thresholds must be quantitatively defined for the UAS to autonomously maintain self-separation. In this paper, the separation thresholds are defined quantitatively for the UAS in a dynamic airspace full of aircrafts that differ in motion state and performance. Then, a “sector-like” dynamic collision-free region (CFR) was set up around the UAS. The size of the CRF can be adjusted adaptively according to the relative motion states of the surrounding intruders, the performance of the UAS, and the altitude of the airspace. The simulation results show that the proposed adaptive separation thresholds adapted to the dynamic airspace environment better than the fixed separation thresholds recommended by Sense & Avoid Science and Research Panel (SARP), and controlled the missing and false alarm rates on low levels. This means our adaptive separation thresholds can effectively balance the safety and operation efficiency of the airspace.

Safety Assessment of a UAV CD&R System in High Density Airspace Using Monte Carlo Simulations

This paper presents a safety assessment method for unmanned aerial vehicle (UAV) operations, including the effect of a distributed conflict detection and resolution (CD&R) system, in a high density airspace environment. Here, the expected conflicts occurrence and chances for each CD&R system to perform are sufficiently high to extract two parameters of safety, i.e., the frequency of near mid-air collision (NMAC) and the frequency of mid-air collision (MAC), by series of Monte Carlo simulations. The results are then used to derive the safety parameters in a more realistic, less dense airspace. Two cases of distributed CD&R protocols are assessed and compared, i.e., 1) uncoordinated protocol, where each vehicle has its own avoidance preferences, and 2) implicitly coordinated protocol where each vehicle, while still independent from each other, applies simple common rules. Using those CD&R protocols, the result shows a reduction of more than 94% of possible NMAC. Moreover, while maintaining the target level of safety in the airspace, the maximum number of UAVs under an implicitly coordinated CD&R protocol can be at least ten times more than cases when no CD&R is applied.

UAS Integration into the NAS: An Examination of Baseline Compliance in the Current Airspace System

As a result of the FAA Modernization and Reform Act of 2012, Unmanned Aerial Systems (UAS) are expected to be integrated into the National Airspace System (NAS) by 2015. Several human factors challenges need to be addressed before UAS can safely and routinely fly in the NAS with manned aircraft. Perhaps the most significant challenge is for the UAS to be non-disruptive to the air traffic management system. Another human factors challenge is how to provide UAS pilots with intuitive traffic information in order to support situation awareness (SA) of their airspace environment as well as a see-and-avoid capability comparable to manned aircraft so that a UAS pilot could safely maneuver the aircraft to maintain separation and collision avoidance if necessary. A simulation experiment was conducted to examine baseline compliance of UAS operations in the current airspace system. Researchers also examined the effects of introducing a Cockpit Situation Display (CSD) into a UAS Ground Control Station (GCS) on UAS pilot performance, workload and situation awareness while flying in a positively controlled sector. Pilots were tasked with conducting a highway patrol police mission with a Medium Altitude Long Endurance (MALE) UAS in L.A. Center airspace with two mission objectives: 1) to reroute the UAS when issued new instructions from their commander, and 2) to communicate with Air Traffic Control (ATC) to negotiate flight plan changes and respond to vectoring and altitude change instructions. Objective aircraft separation data, workload ratings, SA data, and subjective ratings regarding UAS operations in the NAS were collected. Results indicate that UAS pilots were able to comply appropriately with ATC instructions. In addition, the introduction of the CSD improved pilot SA and reduced workload associated with UAS and ATC interactions.

Ecological Interface Design of a Tactical Airborne Separation Assistance Tool

In a free-flight airspace environment, pilots have more freedom to choose user-preferred trajectories. An onboard pilot support system is needed that exploits travel freedom while maintaining spatial separation with other traffic. Ecological interface design is used to design an interface tool that assists pilots with the tactical planning of efficient conflict-free trajectories toward their destination. Desired pilot actions emerge from the visualization of workspace affordances in terms of a suitable description of aircraft (loco)motion. Traditional models and descriptions for aircraft motion cannot be applied efficiently for this purpose. Through functional modeling, more suitable locomotion models for trajectory planning are analyzed. As a result, a novel interface, the state vector envelope, is presented that is intended to provide the pilot with both low-level information, allowing direct action, and high-level information, allowing conflict understanding and situation awareness.

Developing Safety Indicators for New Zealand Airspace

The New Zealand (NZ) government actively sets safety targets for its airspace, and the level of safety traditionally has been measured by accident rates. However, numerous problems have arisen from the use of this indicator. With the Heinrich pyramid of common causation of accidents and incidents, focus has turned to the use of incident data–-particularly loss-of-separation (LoS) incidents between aircraft in airspace–-for safety indicators. This paper uses LoS data from New Zealand, Australia, and the United Kingdom to develop safety indicators specific to a particular airspace. A survey of previous research on factors affecting LoS incidents led to the selection of seven variables: airspace environment, altitude, traffic profile of the pair of aircraft in conflict, presence of one aircraft in altitude transition, geometrical configuration, causal agent (i.e., last human instigator of the incident), and degree of severity or risk of collision (i.e., level of risk reached during the LoS incident). A high-level data analysis is followed by log-linear categorical analysis to determine significant interactions between variables, and, on the basis of these interactions, specific safety indicators are obtained for NZ airspace. Significant interactions include type of airspace and aircraft in altitude transition, type of airspace and conflict geometry, aircraft in altitude transition and conflict geometry, and causal agent and severity. The results can be used to develop refined country-specific safety indicators and allow for targeted methods of improving safety.

Developing Safety Indicators for New Zealand Airspace: Analysis of Loss-of-Separation Incidents

The New Zealand (NZ) government actively sets safety targets for its airspace, and the level of safety traditionally has been measured by accident rates. However, numerous problems have arisen from the use of this indicator. With the Heinrich pyramid of common causation of accidents and incidents, focus has turned to the use of incident data—particularly loss-of-separation (LoS) incidents between aircraft in airspace—for safety indicators. This paper uses LoS data from New Zealand, Australia, and the United Kingdom to develop safety indicators specific to a particular airspace. A survey of previous research on factors affecting LoS incidents led to the selection of seven variables: airspace environment, altitude, traffic profile of the pair of aircraft in conflict, presence of one aircraft in altitude transition, geometrical configuration, causal agent (i.e., last human instigator of the incident), and degree of severity or risk of collision (i.e., level of risk reached during the LoS incident). A high-le...