The L-band Digital Aeronautical Communications System (LDACS), the worldwide first true integrated Communication, Navigation and Surveillance (CNS) system, is in the process of being standardized at the International Civil Aviation Organization (ICAO) and the Internet Engineering Task Force (IETF). The cellular system is considered a successor to the 30-years old Very High Frequency (VHF) Datalink mode 2 system (VDLm2) and intended for communications related to the safety and regularity of flight. With the initial rollout planned in the near future, the finalization of all its aspects, including security is of utmost importance. While previous works presented a cybersecurity architecture for LDACS, including a Public Key Infrastructure (PKI), certificates, a Mutual Authentication and Key Establishment (MAKE) procedure, as well as usage of established keys for protecting its user- and control-data plane, the protocol for secure LDACS handovers between cells has not been established. The objective of this work is to present a secure handover procedure for LDACS, fulfilling all security and performance requirements for data- and voice communications via LDACS.

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This paper presents a machine learning-based approach for predicting the taxi-out time, with the departure process decomposed into two components – the time taken to travel from the gate to the departure queue, and the time spent in the departure queue. Gradient-Boosted Decision Tree (GBDT) models are trained to predict the two components using different feature sets, and a comparison of both model shows that they can provide better prediction accuracy compared with conventional methods, with a Root Mean Squared Error (RMSE) of 1.79 minutes and 0.92 minutes when predicting the taxiing and queuing times respectively, and 78% and 96% of predictions falling within a ±2 minute error margin. Predictions from the GBDT model are analysed and interpreted using SHAP (SHapley Additive exPlanations) values. In particular, the taxiing model identified route features as being the most important feature group, while the queuing model identifies runway queuing features as the most important group. The model explainability provides a pathway towards the certification of machine learning techniques in Air Traffic Controller (ATCO) decision support tools. Finally, a prototype dashboard is presented, providing a visual interface for ATCOs to interpret the model outputs, plan the departure sequence, as well as to analyse the causes of airport delays.

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Apron operations must ensure both high utilization of given capacity and safe aircraft operations even under degraded environmental conditions, such as low visibility. An appropriate sensor environment could support controllers, where deep learning models will ensure that the observed objects are classified correctly. The fundamental challenge is that these models require a large amount of data to be trained. Therefore, we have developed a virtual airport to generate the required training and validation data at any time and for any operational scenario (ground truth). We apply our concept of a virtual airport and sensor environment at Singapore Changi Airport implementing a synthetic LiDAR sensor. With the help of different data sources and own models, a multitude of 3D scenes can be generated which correspond to the real operational environment. From these scenes, a point cloud is extracted according to the specifications of the LiDAR sensor, which is already labeled by the underlying model and serves as input for PointNet++ for segmentation and classification. We show that the training of a classifier based on artificial input data is a promising approach, which covers relevant aspects of the real system and can therefore be easily applied in (augmented) tower environments.

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Security checking is a major issue in airport operations. Affecting the correct number of security agents is essential to provide a good quality of service to passengers while providing the best security performances. At Paris Charles de Gaulle airport the affectation of security agents is decided at strategical level, more than a month in advance. The key element to determine the number of agents needed is the passenger flow through the security checkpoints. This flow is correlated to the passenger flow in the different boarding rooms. This paper investigates the interest of small dense neural networks to perform passenger flow prediction at strategical level for Paris Charles de Gaulle airport. A dense neural network has been trained to predict the passenger flow for each boarding room of the airport. The network has been compared to a more complex long short-term memory model in terms of mean absolute error and outperformed a mathematical model based on exponentially modified Gaussian distribution.

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Today, satellite navigation is the primary means of navigation in civil aviation. Global Navigation Satellite Systems offer global coverage, high accuracy, and reliable position information. They are not only used for en-route navigation but there are also approach procedures based on satellite navigation and safety systems like the Terrain Avoidance and Warning System, that depend on its reliable data. However, Global Navigation Satellite Systems also have their weaknesses; as the signals from space are very weak, they can easily be disturbed. In recent years, reports about so-called radio frequency interference strongly increased which poses a problem for civil aviation. Airlines have identified the risks associated with radio frequency interference but data on such events is scarce. This paper analyzes Automatic Dependent Surveillance-Broadcast data of flights where cockpit crews reported issues that are in line with the expected effects of radio frequency interference. The analysis showed that the effects of radio frequency interference are clearly visible in the Automatic Dependent Surveillance-Broadcast messages, which therefore can serve as a useful source of information to get a holistic picture about the extent of radio frequency interference. Together with flight crew reports describing the effects in the cockpit for different aircraft types, the operational impact of such jamming events can then be assessed.

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Automatic dependent surveillance broadcast technology is transforming aviation by providing air traffic controllers with fast and accurate surveillance capabilities which rely on aircraft broadcasting their position obtained typically through the global navigation satellite system. While satellite navigation enables accurate positioning, it remains vulnerable to external disturbances, which can be triggered by multiple causes, such as intentional jamming or ionospheric events. In such cases, navigation systems revert to less accurate navigation techniques, such as inertial sensors or ground-based radio navigation aids. This study comes with two main objectives: (i) to study the impact of an outage of satellite navigation systems on mid-air collision risk due to navigation performance degradation, and (ii) to translate collision probabilities due to navigation error into minimum spacing requirements. In this context, this study simulates navigation errors of real world trajectories by using a probabilistic radio navigation error model. Results show that the risk of mid-air collisions is increased in case of a degradation of the navigation performance. However, it stays well below the target level of safety when ground-based navigation aids are used instead of satellite navigation.

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Satellite-based augmentation system (SBAS) is an integral part of modern-day aeronautical navigation. Recently, the dual-frequency multi-constellation (DFMC) SBAS based on the L1 and L5 frequencies (1575.42 MHz and 1176.45 MHz) have received a wide attention. However, the ionosphere-free combination (IF) technique used in DFMC SBAS brings about relatively higher noise levels compared with the L1 frequency-only approach. Importantly, the suitable radius (distance) between the user and reference receivers needs to be investigated for the preliminary SBAS corrections and performances. Therefore, in this work, we investigate the long-term correction (LTC) parameters generated from three reference stations in Thailand. The positioning performances of DFMC SBAS demo (GPS and Galileo satellites) are investigated. The positioning results are calculated based on the single point positioning (SPP) algorithm. Using the estimated LTC parameter (radius of ~587 km) on quiet days, the positioning errors in horizontal and vertical directions are 1.38 and 2.59 m, respectively. In addition, on disturbed days, the horizontal and vertical errors are 1.50 and 2.93 m, respectively. From the study, the reference stations in the radius of ~600 km can be considered a suitable range for the DFMC SBAS demo at Thailand location.

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The System Wide Information Management (SWIM) provides a digital data-sharing infrastructure that includes the standardization of the data format and exchange protocol internationally so that the required information can be shared among related civil aviation stakeholders efficiently and safely. In our project, we study availability of the Aeronautical Mobile Airport Communications System (AeroMACS) as a wireless media to achieve Aircraft Access to SWIM (AAtS) around the airport. We carried out flight experiments by AeroMACS prototype and SWIM test bed over Sendai City to confirm the possibility of expanding of AeroMACS coverage. In this report, we focus on tracking, antenna placement and handover evaluations. First, the results of tracking tests show that our system can track the aircraft by using only ADS-B position report and keep the communication link between the base station and the aircraft. Second, the results of antenna placement tests show that both the main and sub antennas for large aircraft should be mounted at the bottom of the aircraft body to obtain higher combined gain. On the other hand, the main and sub antennas for small aircraft should be mounted at the bottom and top of the aircraft respectively to obtain diversity gain during the aircraft turn. Third, the results of handover tests show that our system needs parameter optimization regarding handover in the air. Finally, we demonstrate SWIM-based information sharing over extended AeroMACS coverage.

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This contribution discusses the imaging of large antenna array navigation systems by means of nearfield measurements. Examples for such navigation systems are the instrument landing system (ILS) and the Doppler VHF omnidirectional radio range (DVOR). In this context imaging means the determination of individual array’s antenna’s amplitudes and phases with nearfield measurements and a corresponding prediction of the far field radiation characteristic according to required specifications of flight inspections. Nearfield measurement results of a large ILS antenna array are presented, and fundamentals are explained with numerical simulations. In particular, a simple simulation scheme allows the investigation of basic measurement requirements and errors that are essential for the later application. This work is part of the NAVANT-NG II (navaid antenna characterization – next generation) project dealing with nearfield inspection techniques by means of unmanned aerial vehicle. From the academic point of view this contribution summarizes the fundamental issues and probable approaches for solutions of such imaging techniques, which are a mathmatical ill-conditioned problem, that exceeds the current state of the art of nearfield inspection of single antennas.

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Multipath propagation can lead to signal disturbances and cause risks in the operation of critical applications such as flight navigation systems. Hence, in addition to an effective system design, an important task is to ensure a sufficiant level of system integrity. This e.g. requires to identify possible sources of interference. The presented work investigates the impact of non-static scatterers on transmission channels. In particular, the focus is on periodic interferences caused by fast rotating propellers of the military aircraft Airbus A400M on the localizer of the instrument landing system (ILS). Measurement results are presented for a generic triangular setup and a realistic ILS scenario with an aircraft on various positions around the runway.

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This paper proposes a method to generate an integrated air-rail timetable at a hub airport with direct access to a train station. A passenger-oriented metric is introduced to assess the connection time between trains and flights. In order not to impact severely initial flight and train schedules, only small perturbations on the initial timetable are authorized. An integer linear programming formulation is proposed based on this metric. An approached resolution method is implemented to solve the optimization problem. Solution quality and computational time are compared with an exact resolution method. Computational results on the case study of Paris-Charles de Gaulle airport are presented. Results show that a change of an average 11 minutes in schedules could increase passenger comfort by almost 10%.

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The runway system of an airport is a bottleneck resource, limiting the amount of traffic an airport can service. The capacity of a runway system is affected by the runway configuration in use and the transition time to change to a new runway configuration. Better prediction of runway configuration transition times can aid air traffic controllers in selecting the runway configuration that minimises delays. This study introduces a novel data-driven approach to model the transition times between directional runway configuration changes, derived by using computed features from the flight positional data. The study also formulates classification models to assign the magnitude of transition times and their impact on runway capacity, utilizing features known in the literature, as well as engineered features including weather coefficients and runway complexity. The transition time model is able to identify the instances where the transition times are ‘High’ approximately 92% of the time. Correctly identifying ‘High’ transition times is important as high transition times lead to greater reduction in runway capacity. This is validated when the predicted transition time is used as a feature input for the capacity impact model, which correctly identifies periods of unfulfilled demand approximately 89% of the time. The predicted transition time is shown to be a more important predictor of capacity impact than weather features, which to date have been considered crucial features in determining capacity.

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This study proposes an autonomous aircraft taxi-agent that can be used to recommend the pilot the optimal speed profile to achieve optimal fuel burn and to arrive on time at the target position on the taxiway while considering potential interactions with surrounding traffic. The problem is modeled as a control decision problem which is solved by training the agent under a Deep Reinforcement Learning (DRL) mechanism, using Proximal Policy Optimization (PPO) algorithm.1) The reward function is designed to consider the fuel burn, taxi-time, and delay-time. Thus, the trained agent will learn to taxi the aircraft between any pair of locations on the airport surface timely while maintaining safety and efficiency. As the result, in more than 97.8% of the evaluated sessions, the controlled aircraft can reach the target position with the time difference within the range of [-20,5] seconds. Moreover, compared with actual fuel burn, the proposed autonomous taxi-agent demonstrated a reduction of 29.5%, equivalent to the reduction of 13.9 kg of fuel per aircraft. This benefit in fuel burn reduction can complement the emission reductions achieved by solving other sub-problems, such as pushback control and taxi-route assignments to achieve much higher performance.

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Aircraft crew are aware of the delay they have experienced at departure. However, uncertainties ahead, and in particular holdings at arrival, can have an impact on the final performance of their operations. When optimising a trajectory the expected cost at the arrival gate should be considered. Consequently, taking into account potential congestion and extra delay at the arrival airspace is paramount to avoid taking sub-optimal decisions at the early stages of the flight. This paper presents a framework to optimise trajectories in the execution phase of the flight considering expected delays at arrival. A flight from Athens (LGAV) to London Heathrow (EGLL) is used as illustrative example, systematically exploring a range of departure delays and expected holdings at arrival.

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Strategic 4D trajectory planning is a promising technology for next-generation air traffic management and systems. Some approaches attempt to satisfy the capacity constraint to reduce traffic congestion, while others aim to reduce potential conflicts between trajectories. This paper investigates two approaches to organizing the real traffic in the French airspace at the strategic level. The first approach minimizes interaction between trajectories, while the second reduces traffic congestion so that the controller maintains the traffic without much effort. The associated optimization problems are formulated and resolved by an approximative approach based on simulated annealing. The departure time perturbation was introduced to study the robustness of the two proposed methods. The evaluation of the robustness is performed by Monte Carlo simulation. According to the results, the strategic deconfliction method completely solved all interactions between trajectories, and the strategic decongestion method reduced traffic congestion by 99.94%. Furthermore, the comparative study shows that the method reducing congestion is more robust against the departure time perturbation than the method minimizing interaction between trajectories. These findings encourage the appropriate use of proposed methods in the strategic 4D trajectory planning framework.

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Slot allocation in a single airport aims to maximize the utilization of airport declared capacity, while slot allocation in a multi-airport system (MAS) has to take airspace capacity into account. Because the limited capacity of certain departure/arrival fixes in the terminal airspace can cause unnecessary flight delays. The uncertainty of flying time between airport and congested fixes makes it even more complicated for slot allocation in a MAS. Traffic flow may be over capacity when the flying times of flights change. In this paper, we propose a mixed integer-programming model for slot allocation in a MAS. The objective of the model is to minimize the total displacements of flights in the MAS while considering all the capacity constraints as well as the uncertainty of flying time. The constraints at departure/ arrival fixes are transformed into chance constraints, and Lyapunov theorem is applied for the transformation. To test the proposed model, a case study of schedule optimization in the MAS of Guangdong-Hong Kong-Macao Greater Bay is presented. Specifically, the impact of the uncertainty of flying time from five airports to airspace fix YIN is investigated. Results show that the total displacements increased if the uncertainty of flying time was considered. The optimized schedule, however, is more robust which can satisfy capacity constraints in various scenarios.

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Runway segmentation for polarized synthetic aperture radar has been a problem of great interest in the field of synthetic aperture radar imagery. Its goal is to segment the radar image in order to determine the exact location of the runway and its unique morphology, which is crucial for both military and civil purposes. The extraction of runways for synthetic aperture radar pictures still relies heavily on conventional methods because of the issue of a little amount of data. However, the segmentation model of the convolutional neural network, which is popular in the segmentation task of optical pictures, has the advantages of high accuracy and versatility. Therefore, this paper proposes an airport runway segmentation algorithm based on Dilated U-net network for synthetic aperture radar, which can accurately extract airport runways even with a small amount of data using deep learning methods. This algorithm combines dilated convolution with a U-net network to extract the runway region. The addition of dilated convolution gives the original U-net network a larger perceptual field in the process of feature extraction, which is necessary for airport runway segmentation with a connected structure. After comparison experiments, the algorithm in this paper uses a deep learning method under the circumstance of a small amount of data to increase the accuracy of detection results and also reduce false alarms and missed alarms.

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Placement of master stations and receiving stations are localized around San Salvador’s airport for a plausible implementation of a terminal area multilateration scheme, the theoretical performance of the multilateration system is shown with the aid of the horizontal dilution of precision for a synchronization error of 20 ns and 1 ns. The tentative locations for multilateration receiving stations in El Salvador’s international airport offer a good horizontal dilution of precision for different altitudes, especially if a 1 ns synchronization system is chosen, which can benefit the surveillance of El Salvadorian air traffic as we can achieve HDoP values below 6, the obtained theoretical result shows the possibility of multilateration in El Salvador’s international airport. .

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A “detect-and-avoid” capability providing a” remain well-clear” function will be needed for unmanned aircraft to fly safely in uncontrolled airspace but could also be used in controlled airspace, provided that the action chosen by the pilot, based on the system suggestive guidance, is compatible with the current air traffic control clearance or an amended clearance. The study reported in this paper looked at the potential operational consequences of the interaction between the pilot and the controller in controlled airspace when a clearance amendment must be requested by the pilot, using the recently standardized detect-and-avoid system, ACAS Xu. Simulations on a selected encounter in Japanese airspace were performed, first with only the unmanned aircraft equipped with ACAS Xu and the other aircraft equipped with current collision avoidance system, then with both aircraft equipped with ACAS Xu. A model of pilot behavior when faced with a remain-well-clear alert (including pilot-controller communication delays) was used. The analysis of the qualitative results outlined three areas of concern: the possible confusion caused by multiple changes of remain-well-clear guidance, the undesirable effects when both aircraft are equipped with ACAS Xu and the potential interaction with short term conflict alerts displayed on the controller working position.

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Air traffic flow management balances strategically demand and capacity by applying various initiatives such as ground delay programs and controlled enroute delays. The delay assigned to each flight is determined by the estimated time of arrival and the maximum allowed airborne delay (buffer) set to absorb uncertainties and minimize arrival runway throughput and capacity loss. Current operations often use a constant buffer regardless of the projected traffic. This research uses high-fidelity traffic simulations to investigate the effect of a dynamically-selected buffer optimizing the daily flow. Three metrics are introduced to measure the performance- ground delay, airborne delay and capacity loss. Simulations over 162 days of traffic show the potential for considerable savings using the proposed method. Furthermore, initial feasibility investigation of machine learning applied to the dynamic buffer selection problem is performed and it is concluded that despite a certain loss of optimality and estimation accuracy challenges, such techniques can be potentially used in real-life implementation.

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Over the past several years, the expansion of the aviation industry has created serious environmental challenges. By using aircraft trajectory optimization to reroute climate-sensitive areas, there is a potential to reduce non-CO2 climate effects, which account for approximately two-thirds of aviation radiative forcing. However, as the determination of such climate hotspots and aircraft trajectories requires meteorological variables obtained from weather forecasts, they are affected by uncertainty. In addition, there is no climate policy for aviation non-CO2 emissions in the current planned market-based instruments, implying that rerouting sensitive areas to climate increases the operational costs as the aircraft tends to fly longer routes. To this end, this study proposes the determination of robust aircraft trajectories with objectives ranging from cost optimal to climate optimal routing options, accounting for uncertain meteorological conditions. To motivate airliners to utilize climate optimal routing strategy, the obtained robust trajectories are then assessed in terms of considering charges for emitting in highly climate-sensitive regions. It is shown that by including the cost of climate impact in the operational cost, it is possible to find ”win-win” scenarios in which both the operational cost and the climate impact are reduced. The uncertainty analysis shows that as we increase the charges for emitting in climate hotspots, the uncertainty in operational cost increases, which needs to be considered while setting up market-based instruments for non-CO2 emissions. Such an increase in uncertainty is related to the effects of uncertain meteorological conditions, mainly relative humidity, on quantifying the non-CO2 climate effects.

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The shortest descent time and the lowest fuel consumption are the focus of CDO trajectory optimization research. Generally, the CDO trajectory optimization based on the best single parameter has certain limitations. In this paper, a direct optimization method of CDO trajectory based on the integrated parameters of fuel consumption and total flight time is presented, namely the CDO trajectory segmentation optimization method based on cost index with Gaussian pseudospectral method. Theoretical analysis indicates that an optimal descent trajectory can be easily found, through track segmentation and CI compromise. At same time, the CDO trajectory optimization should mainly study the optimization of segment 1 and segment 2. The results of status analysis are shown that CDO trajectory optimization based on cost index is the optimum.

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The approach briefing is of major importance in commercial aviation. Conducted by the flight crew, it ensures a thorough and mutual understanding of the upcoming descent and approach phase. With regard to a future implementation of reduced crew operations (RCO), an AI-based system is currently being developed that is able to follow the spoken approach briefing, check for its completeness and inform the pilot about possibly missing items. This paper describes the language processing part of the overall system. A commercially available automatic speech recognition system is trained on aviation specific vocabulary and strategies for dealing with cockpit noise are discussed. Steps towards a possible certification of the system according to the European Union Aviation Safety Agency (EASA) Artificial Intelligence Roadmap are outlined.

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This paper addresses large-scale flight planning via a divide-and-conquer technique that exploits the partial separability feature of the problem. 4D-interaction between flights is used to cluster the flights, and these clusters are then exploited to improve the optimization process. Preliminary computational experiments on the French airspace demonstrate the natural separability of air traffic and yield promising computational improvement for flight planning thanks to the clustering.

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Uncertainties inherent to convective weather represent a major challenge for the Air Traffic Management system, compromising operational safety and increasing costs. In this work, we address the multi-aircraft trajectory planning problem around stochastic storm cells. We implement an Augmented Random Search methodology to deform a nominal set of trajectories and look for a feasible solution. Its main objective is to guarantee minimum separation between vehicles and reduce time in risky regions. Through parallel programming on graphical processing units (GPUs), we reduce computational times to enable near-real time operation. We test the algorithm with two aircraft flying at the same airspeed and flight level; the scenario consists of real weather data given by an ensemble forecast. The influence of the maximum number of iterations is analyzed. Results reveal that our algorithm is able to avoid thunderstorms, solve conflicts between aircraft and reduce flight time in a few seconds. Key Words : Thunderstorm Avoidance, Multi-Aircraft, Conflict Solving, Parallel Programming.

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Dynamic Airspace Sectorization (DAS) is a key pathway for enabling advanced demand capacity balancing (DCB) in modernizing Air Traffic Management (ATM). By splitting and merging the sectors, DAS allows airspace to accommodate the evolving air traffic situations for improving the utilization of airspace in response to different air traffic demands, airspace capacity, weather events and other factors. This research aims at supporting the decision-making on when-to-do such DAS from a deep learning perspective. To this end, this paper proposes a multi-task learning (MTL) approach which is able to predict sector traffic flow and airspace capacity simultaneously using a shared neural network architecture. Specifically, the proposed model predicts the demand-capacity imbalance and identifies the opportunity for sector split/merge implementation. To validate the feasibility of the proposed model, a case study has been carried out in Singapore en-route airspace using the Automatic Dependent Surveillance – Broadcast (ADS-B) data and meteorology data in December 2019. Experimental results explicitly show the capability of the proposed MTL model in predicting flow and capacity. Based on predicted results along with a pre-defined rule, the proposed model predicts the demand-capacity imbalance across multiple timescales and explores the potential to facilitate DAS in terms of tactic, pre-tactic and strategic ATM operations.

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Aviation contributes to global warming through the emission of carbon dioxide (CO2) and other non-CO2 effects. The climate impact associated with non-CO2 species highly depends on atmospheric location and time of the emissions. Hence, they can be mitigated by efficient climate-aware trajectory planning to avoid climate-sensitive regions. However, the increase in demand around climate hotspots caused by adopting individually optimized trajectories in a climate-friendly manner may not be practical due to the limited capacity of airspace. Consequently, the actual mitigation potential of climate impact needs to be analyzed at the network level to assess how the adoption of climate optimized trajectories affects Air Traffic Management (ATM) system performance. In this paper, we aim to study the effects of employing climate optimized trajectories on traffic demand. In this regard, taking climate impacts into account, aircraft trajectory optimization is performed for a scenario with 1006 flights in free-route airspace. Uncertainty in the meteorological conditions, as an essential factor affecting aircraft trajectories and estimated climate impacts, is addressed by performing ensemble trajectory prediction. The traffic demand for the optimized aircraft trajectories considering different routing options (ranging from costoptimal to climate optimal) is then assessed. The results show that as we move toward trajectories with lower climate impacts, in addition to the increase in the operational cost, the demand is considerably increased in the sectors adjacent to climate hotspots.

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Abstract— An arrival flight starts to transit from the cruise phase to the descent phase at the top of descent (TOD). Pilots get to know the TOD locations via onboard devices, while controllers can estimate the TOD locations with the help of radar surveillance and simple rules. In order to help controllers to get a better situation awareness of the traffic surrounding an aerodrome, it is of great operational importance to get an accurate prediction of the TOD locations for arrival flights. In this paper, we propose to apply deep learning for TOD location prediction for arrival flights. To do so, a TOD-specific feature engineering is suggested and applied to historical flight trajectories. Then the simple yet effective multilayer perceptron neural network model is adopted for TOD prediction. A case study on the arrival flights to Singapore Changi airport with respect to one-month historical trajectory data is carried out. Experiments demonstrate that the adopted deep learning method is effective for TOD location prediction. When compared against several typical machine learning models for regression, the adopted model yields a mean square error of 0.0039, which is smaller than the error achieved by the comparison models. Meanwhile, the adopted deep learning model yields TOD location prediction errors of 0.29 nautical miles (NM) on average with a standard deviation of 46.88 NM.

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The sporadic E (Es) layer is a layer in the ionospheric E region with a dense electron density and thin altitudinal thickness at altitudes around 100 km. It has been pointed out by International Civil Aviation Organization (ICAO) that the Es layer has a potential to introduce anomalous long-range propagation in the frequency band of aeronautical navigation and communication (EsAP: Es Anomalous Propagation). In order to reveal the detailed characteristics of EsAP, we have constructed a network of instruments for continuously monitoring the intensities of radio waves of various VOR and ILS LOC stations in Japan. In this paper, we introduce the following latest results obtained using data from this monitoring observation: 1) derivation of statistical characteristics (seasonal/local time dependence) of EsAP occurrence probability, 2) mapping of the spatial structure of Es in 2D by combining EsAP observations with GPS-TEC ROTI data, and 3) detailed investigation of the long-range propagation of ILS-LOC signal from Taiwan. We also discuss the possibility for using the anomalous propagation of VHF NAV signal for studies of equatorial plasma bubbles, a different phenomenon in the ionospheric F region.

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The current Air Traffic Control (ATC) system is heavily reliant on voice communication that is not sufficient to fulfil the requirements of Global Air Traffic Management Operational Concept (GATMOC). To improve the safety, operating economics, and environmental sustainability, the concept of Trajectory-Based Operation (TBO) has been proposed to coalesce the ATM components during tactical, planning and flight operations by coordinating the view of the trajectory between different actors in a collaborative environment. In order to validate the concept and promote the shift from current voice-based operation to TBO, the Multi-Regional TBO Demonstration (MR TBO) project has been conducted by the Federal Aviation Administration (FAA). As a technical supporter of the Japan Civil Aviation Bureau (JCAB), the Electronic Navigation Research Institute (ENRI) developed a SWIM test environment that provides simulation capabilities for demonstrations. In this paper, the observations and analysis of demonstration consisting of scenario discussion and function development for TBO implementation is reported. Moreover, the coordination method and information exchange between SWIM-based services in post-departure phase for how to use managed trajectories is discussed. Finally, the lessons learned and challenges for trajectory sharing, management, and utilization are analyzed.

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