National Airspace System Research Articles

Overview of acoustic detection for counter unmanned aircraft systems

Integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) is currently a major problem for government and industry. The detection of unauthorized UAS activity is a concern. There is a need for effective low-cost sensor technology to detect and track unauthorized UAS operations. Acoustic sensor systems have potential to improve counter UAS (C-UAS) sensing capabilities at greatly reduced cost relative to traditional sensors such as radar and optical systems, but the commercially available acoustic sensing technology is still lacking. The MITRE Corporation has been a leader in advising the United States’ Federal Government on the issues around integration of UAS into the NAS and C-UAS technologies. Research done by MITRE on acoustic sensing of UAS will be presented. The current state of commercially available acoustic sensor systems will be briefly reviewed. Recommendations that MITRE has developed on how to design acoustic sensor systems for C-UAS will be discussed. Future research directions in topics such as acoustic beamforming, array design, and outdoor sound propagation will be recommended. Approved for Public Release; Distribution Unlimited 18-4035

Meteorological Impacts on Commercial Aviation Delays and Cancellations in the Continental United States

AbstractWeather creates numerous operational and safety hazards within the National Airspace System (NAS). In 2014, extreme weather events attributed 4.3% to the total number of delay minutes recorded by the Bureau of Transportation Statistics. When factoring weather’s impact on the NAS delays and aircraft arriving late delays, weather was responsible for 32.6% of the total number of delay minutes recorded. Hourly surface meteorological aviation routine weather reports (METARs) at major airports can be used to provide valuable insight into the likely causes of weather delays at individual airports. When combined with the Federal Aviation Administration’s (FAA’s) Operations Network (OPSNET) delay data, METARs can be used to identify the major causes of delays and to create delay climatologies for a specific airport. Also, patterns for delays and cancellations for the study period of 2003–15 can be identified for the individual airports included in this study. These patterns can be useful for operators and airport planners to optimize performance in the future.

Demonstration of a Framework for Comparing Aviation Environmental Impact Mitigation Strategies

By modeling compatibilities, benefits, and penalties of various technology packages at the subsystem level in a vehicle design tool, bottom-up assessments of projected technological benefits can be conducted. Fleet-level exploratory forecasting requires linking these vehicle level benefits to forecasts of operations in the National Airspace System. By synthesizing a system-wide/fleet-level environmental performance model and a rapid fleet-level noise computation model, the tradeoffs between fuel burn, emissions, and noise can be compared for a variety of technology infusion scenarios and various retirement/replacement strategies. These synthesized models serve as a screening-level tool to identify promising scenarios for technology investments. The best scenarios can then be modeled in higher-fidelity tools to verify the potential gains from the technology investment. A few examples of the screening-level capabilities for fleet-level scenario analysis are demonstrated.

Resilience of the U.S. National Airspace System Airport Network

Natural hazards, such as hurricanes and winter storms, computer glitches and technical flaws, and man-made terror or cyber-physical attacks, can lead to localized perturbations of the U.S. national airspace system airport network (NASAN), which can in turn percolate across the interconnected system. Here we develop and demonstrate an approach to quantitatively characterize the robustness of NASAN, defined as loss of critical functions owing to perturbations, and a quantitative framework to select the most efficient and effective post-hazard recovery strategies. The system-level robustness and recovery strategies rely on network science methods and associated attributes. New insights include the central role of network attributes to robustness and optimal recovery sequences. Characterizations of robustness and fragility can inform what-if plans and proactive design, while recovery strategies developed in advance can support systematic, reliable, and timely bounce-back from hazard-related perturbations. The framework can serve as a baseline over which local information or cost optimization can be superposed.

Safety Assessment and Risk Estimation for Unmanned Aerial Vehicles Operating in National Airspace System

This paper proposes an effective approach for modelling and assessing the risks associated with unmanned aerial vehicles (UAVs) integrated into national airspace system (NAS). Two critical hazards with UAV operations are considered and analyzed, which are ground impacts and midair collisions. Threats to fatalities that result from the two hazards are the focus in the proposed method. In order to realize ground impact assessment, a multifactor risk model is designed by calculating system reliability required to meet a target level of safety for different UAV categories. Both fixed-wing and rotary-wing UAVs are taken into account under a real scenario that is further partitioned into different zones to make the evaluation more precise. Official territory and population data of the operation scenario are incorporated, as well as UAV self-properties. Casualty area of impacting debris can be obtained as well as the probability of fatal injuries on the ground. Sheltering factors are not neglected and defined as four types based on the real scenario. When midair collision fatality risk is estimated, a model of aircraft collisions based on the density of civil flight in different regions over Chinese airspace is proposed. In the model, a relative collision area and flying speed between UAVs and manned aircraft are constructed to calculate expected frequency of fatalities for each province correspondingly. Truthful data with different numbers of UAVs is incorporated in the model with the expected number of fatalities after a collision is included. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS.

Modeling the Effects of Uncertainty on the National Airspace System

This paper presents the first steps toward managing uncertainty and assessing risk within the national airspace system (NAS) by investigating the impact of uncertainty on “flight plan flexibility” (FPF) – a proposed quantitative measure of an aircraft’s ability to adapt its flight plan due to improbable events. First, an air traffic scenario derived from national flight plan data is simulated with an open source BlueSky air traffic control analysis centered on a busy airport. Next, state-space diagrams derived from the aircraft state parameters (i.e., speeds, altitudes, headings), spatial proximities, and surveillance signals are used to construct the FPF metric. Finally, a probabilistic analysis is used to propagate uncertainty in the aircraft positions through BlueSky to observe the resulting uncertainty in FPF through time. Future work will aggregate individual aircraft safety measures and additional metrics into a single system-wide indicator, transitioning from BlueSky to a gate-to-gate simulation for prognostics, and deriving probabilistic models of epistemic and aleatory sources of uncertainty in the NAS from available data.

Bayesian-entropy Network for Information Fusion and Reliability Assessment of National Airspace Systems

The air traffic control (ATC) system is critical in maintaining the safety and integrity of the National Airspace System (NAS). This requires the information fusion from various sources. This paper introduces a hybrid network model called the Bayesian-Entropy Network (BEN) that can handle various types of information. The BEN method is a combination of the Bayesian method and the Maximum Entropy method. The Maximum Entropy method introduces constraints and is given as an exponential term added to the classical Bayes’ theorem. The exponential term can be used to encode extra information in the form of constraints. The extra information can come from human experience, historical data etc. These knowledges, once written in a mathematical format, can be incorporated into the classical Bayesian framework. The BEN method provides an alternative way to consider common data types (e.g., point observation) and uncommon data types (e.g., linguistic description for human factors) in the NAS. The reported work is demonstrated in two example problems. The first example involves an air traffic control network model and the BEN uses information from various sources to update for the risk event probability. The second example is related to the prediction of the cause of runway incursion. A network model studying different sources of error is used to make predictions of the cause of runway incursion. The training and validation data is extracted from existing accident report in the Aviation Safety Reporting System (ASRS) database. The results are compared with that of the traditional Bayesian method. It is found that the BEN can make use of the available information to modify the distribution function of the parameter of concern.

Information Fusion for National Airspace System Prognostics

Expanding Prognostics and Health Management (PHM) from an equipment-centric view to complex large-scale engineering systems is a challenging problem. One example for a large engineering system is the next generation national airspace system (NAS), which is a fully coupled cyberphysical- human system. This paper presents an overview of a NASA University Leadership Initiative (ULI) project which aims to address the safety needs and their technology solutions for future NAS. The ULI is a 5-year collaborative project in which researchers from several universities and commercial entities work together to advance real-time airspace safety concepts. The underlying premise is that it is imperative to be able to assess and predict the evolution of the airspace’s safety state. Towards that end the work envisions to address the following issues: modeling of the airspace using both data-driven and physics based approaches; quantifying and managing uncertainty; advancing prognostics and information fusion algorithms; and understanding and modeling human computer interface. A comprehensive simulation environment is being built that allows for assessment of performance and verification and validation. The paper discusses the various activities and places them into the context of overall NAS safety.

Computational Platform for Analyzing the Safety of the National Airspace System

Safety analysis of complex systems can be carried out using computational models of the subsystems and their interactions. A modeling framework for analyzing the safety of the national airspace operations is described. The framework enables investigators to collaboratively formulate models of the subsystems of the National Airspace System and insert off-nominal operating conditions to assess their impact on the overall system safety. The software is designed to allow popular computational frameworks such as Python and Java to interact with it in a seamless manner. Interfaces to one of the popular commercial computational package are also provided. The software incorporates detailed aspects of the national airspace system infrastructure such as airport taxi ways and runways, approach-departure procedures, jet routes, air traffic control Sector and Center boundaries. It also includes simplified human performance models of controllers, pilots and ground vehicle operators. Performance data for over 400 different aircraft types, derived from a well-known database have also been incorporated. Airspace flight operations data is obtained from the Federal Aviation Administration data feed, and the weather data is derived from the National Oceanic and Atmospheric Administration web site. Two example applications of the software are given.

Online Risk-Based Supervisory Maneuvering Guidance for Small Unmanned Aircraft Systems

To achieve a level of safety equivalent to manned aircraft in the national airspace system, the pilot-in-command of small unmanned aircraft systems should be able to maintain situational awareness and make necessary maneuvers to avoid potential conflicts with nearby air traffic during mid-air encounters. However, due to ever-changing flight environments, the pilot-in-command often mis-estimates collision risks and cannot engage appropriate maneuvers to prevent mid-air traffic from violating the safety boundaries of small unmanned aircraft systems. To fix this problem, an online risk-based guidance method is therefore designed and developed in this paper to quantitatively assess mid-air collision risks and provide online mitigation solutions for detect-and-avoid systems. This will help the pilot-in-command identify hazards and choose appropriate avoidance maneuvers before a safety boundary violation occurs.

Information Content Requirements for Remote Pilot Handover of Control of Unmanned Aircraft Systems in the National Airspace

The objective of the work is to develop recommendations to support the development of information content requirements for integrating unmanned aircraft systems (UAS) into the National Airspace System (NAS). The focus of the work relates to handover of control where positive control of the aircraft is transferred from one crew to another. We reviewed the human factors literature and handover corollaries that exist in the current NAS. We developed a structured approach to identifying information requirements that included consideration of technological context, and then addressed task analysis, human-automation function allocation, and information availability. UAS pilot subject matter expert feedback was leveraged throughout the process. We provide recommendations for the technological and automation capabilities required for safe handover of control in the NAS, and associated information requirements. We discuss how our method provides a structured basis for making design decisions for complex, safety-critical systems.

Activity Theory as a Framework for Integrating Uas Into the Nas: A Field Study of Crew Member Activity During Uas Operations Near a Non-Towered Airport

An Activity Theory framework was applied in investigating the pressing issue of Unmanned Aircraft System (UAS) integration into the National Airspace System. As stated in the FAA’s UAS Operational Approval policy notice, the UAS pilot and/or crew are collectively responsible for successfully exercising see-and-avoid duties. To describe how this is achieved in practice, field recordings of visual observers and other UAS crewmembers were collected during three phases of a long-endurance UAS flight test: takeoff, mid-flight, and landing. Four separate radio communications channels were utilized, and pilots’ workload was offloaded in three ways: takeoff and landing flight dynamics were offloaded to the external pilot, see-and-avoid duties were offloaded to visual observers, and some communications were offloaded to the mission commander. Visual observers relied on a combination of visual perception, communication, and team coordination skills to assist pilots and the mission commander in effectively accomplishing see-and-avoid duties during UAS operations.

Utility of Visual and Auditory Warning Alerting for Traffic Avoidance during UAS Operations

Minimum Operational Performance Standards (MOPS) are being developed to support the integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS). Input from subject matter experts and multiple research studies have informed display requirements for Detect-and-Avoid (DAA) systems aimed at supporting timely and appropriate pilot responses to collision hazards. DAA alerting is designed to inform pilots of potential threats to “DAA well clear”; the two highest alert levels – caution and warning – indicate how soon pilot action is required and whether there is adequate time to coordinate with the air traffic controller (ATC). Additional empirical support is needed to clarify the extent to which warning-level alerting impacts DAA task performance. The present study explores the differential effects of the auditory and visual cues provided by the DAA Warning alert, and performance implications compared to caution-only alerting are discussed.

Estimation of Minimum Alerting Boundaries in Winds for Small Unmanned Aircraft Systems

To achieve a level of safety equivalent to manned aircraft in the national airspace system, the pilot in command of small unmanned aircraft systems should be able to maintain situational awareness and make necessary maneuvers to avoid potential conflicts with nearby air traffic during midair encounters. However, due to ever-changing flight environments, the pilot in command often misestimates conflict risks and cannot engage the appropriate maneuvers to prevent midair traffic from violating the safety boundaries of small unmanned aircraft systems. To fix this problem, a Monte-Carlo-simulation-based method is therefore introduced and designed to quantitatively determine the alerting threshold boundaries in various wind conditions for detect-and-avoid systems to help the pilot in command identify hazards and trigger avoidance maneuvers before a safety boundary violation occurs.

Well-Clear Recommendation for Small Unmanned Aircraft Systems Based on Unmitigated Collision Risk

Unmanned aircraft systems must demonstrate a capability to sense and avoid air traffic as part of a layered conflict management system to enable safe operations in the National Airspace System. During operations, an unmanned aircraft system should attempt to remain “well clear” to minimize the need for a collision avoidance action. Previously, a well-clear definition was adopted for large unmanned aircraft systems; however, this definition is not appropriate for small unmanned aircraft system weighing less than 55 lb operating at low altitudes. In response, this paper outlines research toward a definition of well clear for small unmanned aircraft systems, based on airborne collision risk, for midterm concepts of operations at low altitudes in nonterminal airspace.

Minimum SysML Representations to Enable Rapid Evaluation using Agent‐Based Simulation

AbstractThe changing operational requirements and demands for new capabilities, often makes it necessary for systems architects to make substantial changes in a system's architecture. However, the traditional systems engineering processes advocate finalization of architecture at early stages of the design process with little or no change at the later stages of the design. The simulation development is often done towards the end of the design phase and thus, the traditional systems engineering approaches do not support a rapid architecture evolution. In this paper, we propose a Model‐Based Systems Engineering (MBSE) process to facilitate rapid evaluation of changes in a system architecture and/or design artifact for complex systems. This process specifies the minimum set of information that needs to be included in system architecture using SysML diagrams with traceability between the diagrams and agent‐based simulation (ABS) models of a complex system. We demonstrate this SysML‐driven analysis approach to perform a rapid evaluation of evolving system architectures using a NASA‐sponsored project to analyze ab initio architectures for the National Airspace System. We also demonstrate how the proposed approach enables early‐on investigation during the architecture development whether an architecture will yield the desired operational benefit.

Integrating SysML and Agent‐Based Modeling for Rapid Architecture Evaluation

ABSTRACTIn the face of the changing operational requirements and demands for new capabilities, it often is necessary for systems architects to make substantial changes in a system's architecture. However, traditional systems engineering processes, where architects finalized architecture during the initial concept development stages and engineers develop system simulations during later lifecycle phases, do not support a rapid architecture evolution. In this article, we propose a model‐based systems engineering (MBSE) process to facilitate the rapid evaluation of changes in systems architecture and/or design artifacts for complex systems. This process specifies the minimum set of information that engineers need to include in systems architecture using SysML diagrams with traceability between the diagrams and the agent‐based simulation (ABS) models of a complex system. By integrating SysML and ABS models early in the development lifecycle, teams can readily trace any future evolution of systems architecture represented by the SysML diagrams to changes in an ABS model and hence can rapidly evaluate the impact of architectural evolution. We demonstrate the application of the proposed process using a NASA‐sponsored case study for developing ab initio architectures of the National Airspace System (NAS) where we achieved significant time savings in developing an ABS model of evolving NAS architecture based on SysML‐ABS integration.

Aerodrome situational awareness of unmanned aircraft: an integrated self‐learning approach with Bayesian network semantic segmentation

It is expected that soon there will be a significant number of unmanned aerial vehicles (UAVs) operating side-by-side with manned civil aircraft in national airspace systems. To be able to integrate UAVs safely with civil traffic, a number of challenges must be overcome first. This study investigates situational awareness of UAVs’ autonomous taxiing in an aerodrome environment. The research work is based on a real outdoor experimental data collected at the Walney Island Airport, the UK. It aims to further develop and test UAVs’ autonomous taxiing in a challenging outdoor environment. To address various practical issues arising from the outdoor aerodrome such as camera vibration, taxiway feature extraction, and unknown obstacles, the authors develop an integrated approach that combines the Bayesian-network based semantic segmentation with a self-learning method to enhance situational awareness of UAVs. Detailed analysis of the outdoor experimental data shows that the integrated method developed in this study improves the robustness of situational awareness for autonomous taxiing.

Assessment of Small Unmanned Aerial Systems Operations in the National Airspace System

Assessment of Small Unmanned Aerial Systems Operations in the National Airspace System

Establishing Training and Certification Criteria for Visual Observers of Unmanned Aircraft Systems

Safe integration of Unmanned Aircraft Systems (UAS) into airspace generally occupied by manned aircraft and other aviation stakeholders is a pressing global challenge. In the United States, efforts are being made to integrate small and large UAS into the National Airspace System (NAS). Whereas regulations for the civil operation of small UAS (25 kg and lighter) have already been adopted, those for larger unmanned systems are still being crafted. Thus, a two-part mixed methods study was conducted to examine three pivotal issues in the safe operation of large UAS: (1) What kind of visual observer skills are needed to execute safe UAS operations; (2) Should visual observers involved in UAS operations receive formal training; and (3) Should visual observers be required to pass a certification exam? In the first phase, subject matter experts identified various vigilance, trajectory estimation and communication skills that were vital to performing visual observer duties successfully and elaborated on their training regimens. In the second phase, survey participants were approximately evenly split on the need for formal classroom/online and hands-on visual observer training. Furthermore, participants generally favored visual observers having to pass a classroom/online certification exam, whereas they were against a practical (hands-on) exam.