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A Machine Learning Approach to Improve Air Traffic Management Initiatives

Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.

Farzan Masrour Shalmani↗
NASA NTRS2015

Technology Candidates for Air-to-Air and Air-to-Ground Data Exchange

Technology Candidates for Air-to-Air and Air-to-Ground Data Exchange is a two-year research effort to visualize the U. S. aviation industry at a point 50 years in the future, and to define potential communication solutions to meet those future data exchange needs. The research team, led by XCELAR, was tasked with identifying future National Airspace System (NAS) scenarios, determining requirements and functions (including gaps), investigating technical and business issues for air, ground, & air-to-ground interactions, and reporting on the results. The project was conducted under technical direction from NASA and in collaboration with XCELAR's partner, National Institute of Aerospace, and NASA technical representatives. Parallel efforts were initiated to define the information exchange functional needs of the future NAS, and specific communication link technologies to potentially serve those needs. Those efforts converged with the mapping of each identified future NAS function to potential enabling communication solutions; those solutions were then compared with, and ranked relative to, each other on a technical basis in a structured analysis process. The technical solutions emerging from that process were then assessed from a business case perspective to determine their viability from a real-world adoption and deployment standpoint. The results of that analysis produced a proposed set of future solutions and most promising candidate technologies. Gap analyses were conducted at two points in the process, the first examining technical factors, and the second as part of the business case analysis. In each case, no gaps or unmet needs were identified in applying the solutions evaluated to the requirements identified. The future communication solutions identified in the research comprise both specific link technologies and two enabling technologies that apply to most or all specific links. As a result, the research resulted in a new analysis approach, viewing the underlying architecture of ground-air and air-air communications as a whole, rather than as simple "link to function" paired solutions. For the business case analysis, a number of "reference architectures" were developed for both the future technologies and the current systems, based on three typical configurations of current aircraft. Current and future costs were assigned, and various comparisons made between the current and future architectures. In general, it was assumed that if a future architecture offers lower cost than the current typical architecture, while delivering equivalent or better performance, it is likely that the future solution will gain industry acceptance. Conversely, future architectures presenting higher costs than their current counterparts must present a compelling benefit case in other areas or risk a lack of industry acceptance. The business case analysis consistently indicated lower costs for the proposed future architectures, and in most cases, significantly so. The proposed future solutions were found to offer significantly greater functionality, flexibility, and growth potential over time, at lower cost, than current systems. This was true for overall, fleet-wide equipage for domestic and oceanic air carriers, as well as for single, General Aviation (GA) aircraft. The overall research results indicate that all identified requirements can be met by the proposed solutions with significant capacity for future growth. Results also illustrate that the majority of the future communication needs can be met using currently allocated aviation RF spectrum, if used in more effective ways than it is today. A combination of such optimized aviation-specific links and commercial communication systems meets all identified needs for the 50-year future and beyond, with the caveat that a new, overall function will be needed to manage all information exchange, individual links, security, cost, and other factors. This function was labeled "Delivery Manager" (DM) within this research. DM employs a distributed client/server architecture, for both airborne and ground communications architectures. Final research results included identifying the most promising candidate technologies for the future system, conclusions and recommendations, and identifying areas where further research should be considered.

Communications↗
NASA NTRS1997

A Cognitive System Model for Human/Automation Dynamics in Airspace Management

NASA has initiated a significant thrust of research and development focused on providing the flight crew and air traffic managers automation aids to increase capacity in en route and terminal area operations through the use of flexible, more fuel-efficient routing, while improving the level of safety in commercial carrier operations. In that system development, definition of cognitive requirements for integrated multi-operator dynamic aiding systems is fundamental. In order to support that cognitive function definition, we have extended the Man Machine Integrated Design and Analysis System (MIDAS) to include representation of multiple cognitive agents (both human operators and intelligent aiding systems) operating aircraft, airline operations centers and air traffic control centers in the evolving airspace. The demands of this application require representation of many intelligent agents sharing world-models, and coordinating action/intention with cooperative scheduling of goals and actions in a potentially unpredictable world of operations. The MIDAS operator models have undergone significant development in order to understand the requirements for operator aiding and the impact of that aiding in the complex nondeterminate system of national airspace operations. The operator model's structure has been modified to include attention functions, action priority, and situation assessment. The cognitive function model has been expanded to include working memory operations including retrieval from long-term store, interference, visual-motor and verbal articulatory loop functions, and time-based losses. The operator's activity structures have been developed to include prioritization and interruption of multiple parallel activities among multiple operators, to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. The model's internal representation has been be modified so that multiple, autonomous sets of equipment will function in a scenario as the single equipment sets do now. In order to support the analysis requirements with multiple items of equipment, it is necessary for equipment to access the state of other equipment objects at initialization time (a radar object may need to access the position and speed of aircraft in its area, for example), and as a function of perception and sensor system interaction. The model has been improved to include multiple world-states as a function of equipment am operator interaction. The model has been used -1o predict the impact of warning and alert zones in aircraft operation, and, more critic-ally, the interaction of flight-deck based warning mechanisms and air traffic controller action in response to ground-based conflict prediction and alerting systems. In this operation, two operating systems provide alerting to two autonomous, but linked sets of operators, whose view of the system and whose dynamics in response are radically different. System stability and operator action was predicted using the MIDAS model.

Corker, Kevin M.↗
NASA NTRS1992

Identification of Important "Party Line" Information Elements and the Implications for Situational Awareness in the Datalink Environment

Air/ground digital datalink communications are an integral component of the FAA's Air Traffic Control (ATC) modernization strategy. With the introduction of datalink into the ATC system, there is concern over the potential loss of situational awareness by flight crews due to the reduction in the "party line" information available to the pilot. "Party line" information is gleaned by flight crews overhearing communications between ATC and other aircraft. In the datalink environment, party line information may not be available due to the use of discrete addressing. Information concerning the importance, availability, and accuracy of party line elements was explored through an opinion survey of active air carrier flight crews. The survey identified numerous important party line elements. These elements were scripted into a full-mission flight simulation. The flight simulation experiment examined the utilization of party line information by studying subject responses to the specific information elements. Some party line elements perceived as important were effectively utilized by flight crews in the simulated operational environment. However, other party line elements stimulated little or no increase in situational awareness. The ability to assimilate and use party line information appeared to be dependent on workload, time availability, and the tactical/strategic nature of the situations. In addition, the results of both the survey and the simulation indicated that the importance of party line information appeared to be greatest for operations near or on the airport. This indicates that caution must be exercised when implementing datalink communications in these high workload, tactical sectors. This document is based on the thesis of Alan H. Midkiff submitted in partial fulfillment of the degree of Master of Science in Aeronautics and Astronautics at the Massachusetts Institute of Technology.

Midkiff, Alan H.↗
NASA NTRS2005

Learning Lessons from the X-37 Project

The X-37 was planned as an automated vehicle capable of flight-testing new aerospace technologies in combined environments that are beyond the capability of existing ground or flight platforms. Flight demonstration with the X-37 architecture and configuration in relevant environments was planned to reduce the risk of developing launch vehicle technologies for sustainable, affordable exploration and other aerospace applications. Current plans are for the X-37 Approach and Landing Test Vehicle (ALTV) to be atmospheric tested in 2005 from Scaled Composite's White Knight carrier aircraft at up to 40,000 feet over California's Mojave Spaceport, with and turnaround maintenance performed. The fight Operations Control Center will conduct the mission, using a streamlined operations concept. Taxi-tow and captive-carry tests will be conducted prior to the atmospheric-test series. Sponsored by the Defense Advanced Research Projects Agency (DARPA) with NASA participation, technical objectives are to: (1) mature Computed Air Data System/Remote Pressure Sensor technology, (2) manage energy during Terminal Area Energy Management/Heading Alignment Cone maneuvers, and (3) validate the aerodatabase. The X-37 Project began in 1999 under a cooperative agreement as an element of NASA's Future X Program and transitioned to a NASA Research Announcement under the Space Launch Initiative. In mid-2004, NASA transferred ownership to DARPA, with its heritage of performing high-risk, high-payoff research and development. NASA contributes technical expertise, including risk analysis and system integration. The Boeing Company is the prime contractor, with nationwide suppliers. This recent partnership exemplifies the synergy attainable when NASA Centers, other Government agencies, and industry work together toward a common goal - contributing to the knowledge base for U.S. exploration and other aerospace endeavors. The X-37 team represents a range of space transportation disciplines - from engineering to management. Some members have been with the project since its inception. All have gained priceless experience during the design, manufacturing, and testing of the ALTV, as well as through developing advanced orbital flight technologies, such as state-of-the-art Thermal Protection Systems and hot structures. Throughout this process, the X-37 Project team captures lessons that are directly applicable to other such efforts. The upcoming ALTV flights offer another dimension of data and first-hand experience that will prove invaluable to those designing new generations of reusable spacecraft. And ongoing technology developments will expand the aerospace knowledge base. Delivering prototype hardware is always a risky proposition. During the course of the X-37 effort, the team has experienced many challenging opportunities, delivering significant accomplishments and learning numerous lessons in the process. The ability to manage the risk landscape is key to overcoming obstacles, especially technical hurdles that are encountered in progressing hardware from design to flight. The approach to managing risk under this partnership is evolving but, in general, the team allocates resources to reduce the likelihood of severe-consequence risks, thus maximizing mission success and ensuring that the X-37 Project delivers value to its stakeholders. As the team sharpens its focus on operations, it continues to contribute knowledge to those who would undertake high-risk, high-payoff research and development and provides valuable experience to implement the exploration vision.

Turner, Susan↗
NASA NTRS2005

Learning Lessons from the X-37 Project

The X-37 was planned as an automated vehicle capable of flight-testing new aerospace technologies in combined environments that are beyond the capability of existing ground or flight platforms. Flight demonstration with the X-37 architecture and configuration in relevant environments was planned to reduce the risk of developing launch vehicle technologies for sustainable, affordable exploration and other aerospace applications. Current plans are for the X-37 Approach and Landing Test Vehicle (ALTV) to be atmospheric tested in 2005 from Scaled Composite s White Knight carrier aircraft at up to 40,000 feet over California's Mojave Spaceport, with landing and turnaround maintenance performed. The Flight Operations Control Center will conduct the mission, using a streamlined operations concept. Taxi-tow and captive-carry tests will be conducted prior to the atmospheric-test series. Sponsored by the Defense Advanced Research Projects Agency (DARPA) with NASA participation, technical objectives are to: (1) mature Computed Air Data System/Remote Pressure Sensor technology, (2) manage energy during Terminal Area Energy Management/Heading Alignment Cone maneuvers, and (3) validate the aerodatabase. The X-37 Project began in 1999 under a cooperative agreement as an element of NASA's Future X Program and transitioned to a NASA Research Announcement under the Space Launch Initiative. In mid-2004, NASA transferred ownership to DARPA, with its heritage of performing high-risk, high-payoff research and development (R&D). NASA contributes technical expertise, including risk analysis and system integration. The Boeing Company is the prime contractor, with nationwide suppliers. This partnership exemplifies the synergy attainable when NASA Centers, other Government agencies, and industry work together toward a common goal - contributing to the knowledge base for U.S. exploration and other aerospace endeavors. The X-37 team represents a range of space transportation disciplines - from engineering to management. Some members have been with the project since its inception. All have gained priceless experience during the design, manufacturing, and testing of the ALTV, as well as through developing advanced orbital flight technologies, such as state-of-the-art Thermal Protection Systems and hot structures. Throughout this process, the X-37 Project team captures lessons that are directly applicable to other such efforts. The upcoming ALTV flights offer another dimension of data and first-hand experience that will prove invaluable to those designing new generations of reusable spacecraft. And ongoing technology developments will expand the aerospace knowledge base. Delivering prototype hardware is always a risky proposition. During the course of this effort, the X-37 team has experienced many challenging opportunities, delivering significant accomplishments and learning numerous lessons in the process. The ability to manage the risk landscape is key to overcoming obstacles, especially technical hurdles that are encountered in progressing hardware from design to flight. The approach to managing risk under this partnership is evolving but, in general, the team allocates resources to reduce the likelihood of severe-consequence risks, thus maximizing mission success and ensuring that the X-37 Project delivers value to its stakeholders. As the team sharpens its focus on operations, it continues to contribute knowledge to those who would undertake high-risk, high-payoff R&D and provides valuable experience to implement the Vision for Space Exploration.

Turner, Susan↗
NASA NTRS2003

Infrared Avionics Signal Distribution using Wavelength Division Multiplexing

Pilots in the cockpits of aircrafts currently communicate with ground stations using Radio Frequency (RF) signals. Antennas mounted outside the aircraft receive and transmit RF signals from and to the ground stations. The RF signals received at the antennas are sent to the cockpit using coaxial cables. As the number of antennas needed to provide more than one frequency band in aircrafts increases, RF distribution media (such as coaxial cable) adds to the complexity and weight of the cockpit wiring. Concomitantly, the safety and signal to noise ratio also decreases due to the use of RF signals. The University of Oklahoma is collaborating with the National Aeronautics and Space Administration to develop optical fiber based schemes to replace the coaxial cable used for RF signal distribution within an aircraft. The project aims at exploiting emerging Wavelength Division Multiplexing (WDM) techniques to reduce the weight of cabling, and increase the signal to noise ratio and reliability. This will be achieved by wavelength division multiplexing the signals from the various antennas and then demultiplexing the signals to recover the original signals at the cockpit. This paper will show that (i) RF signals can not only be wavelength multiplexed at the end of a fiber, but additional signals can be inserted into the middle of the fiber using WDM technology, and (ii) the signals can also be successfully extracted by tapping into the middle of the fiber. We are currently extending our previous laboratory prototype (which could multiplex signals only at the end of the fiber) to include additional multiplexing and demultiplexing of RF signals from the middle of the optical backbone with a view to validating the proof of concept, and carrying out measurements to determine the effectiveness of Wavelength Division Multiplexing for avionics applications. A test bed to perform measurements of several relevant parameters for various modulation schemes and frequencies (such as VHF, UHF, and L-Band) has been implemented. In particular, results of transmitter and receiver noise, bit-error-rate (BER), effect of cross talk on the quality of the multiplexed signals, and Signal to Noise ratio and Carrier to Noise ratio, obtained using the aforementioned test bed, will be presented.

Atiquzzaman, Mohammed↗
NASA NTRS1947

Report on Rocket Power Plants Based on T-Substance

In the search for an energy source independent of air for the propulsion of underwater craft, attention was early concentrated on T-substance. It was possible to convince the OKM [NACA comment: Navy High Command] very quickly of the importance of this material. In 1934, the first experiments were undertaken. A difficulty was at once presented by the limited concentration that had been attained. At first only 60 percent T-substance could be supplied; this amount was later increased to as much as 85 percent. Decomposition and combustion experiments conducted on the grounds of the CPVA in Kiel-Dietrichsdorf led to the first practical information as to the technical feasibility of the use of T-substance. New perspectives soon developed because a method of concentrated energy production had been found here, which was capable of many applications. The idea of using this energy for the propulsion of missiles either in guns or as rockets suggested itself and appropriate proposals, which quickly led to the construction of the first experimental devices, were made to the official quarters concerned. In January 1937, the first flight of a DVL aircraft with T-substance auxiliary propulsion took place at Alimbsmuhle in the presence of Colonel Udet, who piloted the third flight. In June 1937, the first T-substance rockets were fired (Altenwalde). Then in rapid succession take-off auxiliary, main propulsion, and other rocket drives were brought out in experimental versions. Hydrogen peroxide is a well known chemical, which is widely used in the textile industry. Its chemical and physical properties as well as the processes of manufacture and use are familiar and have been described in voluminous books and papers, Nevertheless, much developmental work was required to open the way for T-substance as a usable oxygen carrier. In fact the utilization of hydrogen peroxide as the oxygen carrier for energy production had hitherto been the subject only of isolated suggestions, which have never been developed beyond the stage of theoretical discussion.

Walter, Hellmuth↗
NASA NTRS1999

GPS Ocean Reflection Experiment on Spartan 251

It has recently been demonstrated that the GPS signal which has reflected from the ocean surface contains useful geophysical data from which the sea surface wind speed and other parameters can be extracted. This can be used for remote sensing, similar to present day use of radar altimeters or scatterometers, but with significantly smaller instrumentation because of the utilization of the existing GPS broadcast signal for illumination. Several campaigns of aircraft experimentation have been completed demonstrating this technique and reflected GPS data has been reliably collected from 25 km altitude on a balloon. However, there has not yet been a demonstration that the reflected GPS signal can be detected from orbit with sufficient signal to noise ratio (SNR) to make useful remote sensing measurements. A technology demonstration experiment was planned for a Space Shuttle flight in the late 2000 using the Spartan 251 recoverable carrier. This experiment would also have been the first flight validation of the PiVoT GPS receiver developed in house at the Goddard Space Flight Center. The "open-architecture" design of this receiver would allow the software modifications to be made which control code-correlator spacing to map out the shape of the reflected signal waveform, which is the most basic data product generated by this instrumentation. A moderate gain left-hand circularly polarized antenna, constructed from an array of off-the-shelf hemispherical antennas was to be used to give approximately 3 to 6 dB of additional gain. Preliminary SNR predictions have been done indicating that this antenna would offer sufficient gain to record waveform measurements. A system level description of the experiment instrumentation, including the receiver, antenna and data storage and retrieval will be given. The visibility of GPS reflections over the mission duration of several hours will be studied, including the effects of the limited beamwidth of the antenna. Spartan 251 has now been postponed with the earliest opportunity in the year 2002. The results of this study however, have been 2 used to further the define the requirements and expected performance of reflected GPS receivers in orbit. Several other space flight opportunities are being considered based upon this new information.

Garrison, James L↗
NASA NTRS2006

Fuel Cell Airframe Integration Study for Short-Range Aircraft: Aircraft Propulsion and Subsystems Integration Evaluation - Volume 1

The objective of this study is to define the functionality and evaluate the propulsion and power system benefits derived from a Solid Oxide Fuel Cell (SOFC) based Auxiliary Power Unit (APU) for a future short range commercial aircraft, and to define the technology gaps to enable such a system. United Technologies Corporation (UTC) Integrated Total Aircraft Power System (ITAPS) methodologies were used to evaluate a baseline aircraft and several SOFC architectures. The technology benefits were captured as reductions of the mission fuel burn, life cycle cost, noise and emissions. As a result of the study, it was recognized that system integration is critical to maximize benefits from the SOFC APU for aircraft application. The mission fuel burn savings for the two SOFC architectures ranged from 4.7 percent for a system with high integration to 6.7 percent for a highly integrated system with certain technological risks. The SOFC APU itself produced zero emissions. The reduction in engine fuel burn achieved with the SOFC systems also resulted in reduced emissions from the engines for both ground operations and in flight. The noise level of the baseline APU with a silencer is 78 dBA, while the SOFC APU produced a lower noise level. It is concluded that a high specific power SOFC system is needed to achieve the benefits identified in this study. Additional areas requiring further development are the processing of the fuel to remove sulfur, either on board or on the ground, and extending the heat sink capability of the fuel to allow greater waste heat recovery, resolve the transient electrical system integration issues, and identification of the impact of the location of the SOFC and its size on the aircraft.

Gummalla, Mallika↗
NASA NTRS2012

Performance Data Errors in Air Carrier Operations: Causes and Countermeasures

Several airline accidents have occurred in recent years as the result of erroneous weight or performance data used to calculate V-speeds, flap/trim settings, required runway lengths, and/or required climb gradients. In this report we consider 4 recent studies of performance data error, report our own study of ASRS-reported incidents, and provide countermeasures that can reduce vulnerability to accidents caused by performance data errors. Performance data are generated through a lengthy process involving several employee groups and computer and/or paper-based systems. Although much of the airline indUStry 's concern has focused on errors pilots make in entering FMS data, we determined that errors occur at every stage of the process and that errors by ground personnel are probably at least as frequent and certainly as consequential as errors by pilots. Most of the errors we examined could in principle have been trapped by effective use of existing procedures or technology; however, the fact that they were not trapped anywhere indicates the need for better countermeasures. Existing procedures are often inadequately designed to mesh with the ways humans process information. Because procedures often do not take into account the ways in which information flows in actual flight ops and time pressures and interruptions experienced by pilots and ground personnel, vulnerability to error is greater. Some aspects of NextGen operations may exacerbate this vulnerability. We identify measures to reduce the number of errors and to help catch the errors that occur.

countermeasures↗
NASA NTRS2013

Millimeter-Wave Localizers for Aircraft-to-Aircraft Approach Navigation

Aerial refueling technology for both manned and unmanned aircraft is critical for operations where extended aircraft flight time is required. Existing refueling assets are typically manned aircraft, which couple to a second aircraft through the use of a refueling boom. Alignment and mating of the two aircraft continues to rely on human control with use of high-resolution cameras. With the recent advances in unmanned aircraft, it would be highly advantageous to remove/reduce human control from the refueling process, simplifying the amount of remote mission management and enabling new operational scenarios. Existing aerial refueling uses a camera, making it non-autonomous and prone to human error. Existing commercial localizer technology has proven robust and reliable, but not suited for aircraft-to-aircraft approaches like in aerial refueling scenarios since the resolution is too coarse (approximately one meter). A localizer approach system for aircraft-to-aircraft docking can be constructed using the same modulation with a millimeterwave carrier to provide high resolution. One technology used to remotely align commercial aircraft on approach to a runway are ILS (instrument landing systems). ILS have been in service within the U.S. for almost 50 years. In a commercial ILS, two partially overlapping beams of UHF (109 to 126 MHz) are broadcast from an antenna array so that their overlapping region defines the centerline of the runway. This is called a localizer system and is responsible for horizontal alignment of the approach. One beam is modulated with a 150-Hz tone, while the other with a 90-Hz tone. Through comparison of the modulation depths of both tones, an autopilot system aligns the approaching aircraft with the runway centerline. A similar system called a glide-slope (GS) exists in the 320-to-330MHz band for vertical alignment of the approach. While this technology has been proven reliable for millions of commercial flights annually, its UHF nature limits its ability to operate beyond the 1-to-2-meter precisions associated with commercial runway width. A prototype ILS-type system operates at millimeter-wave frequencies to provide automatic and robust approach control for aerial refueling. The system allows for the coupling process to remain completely autonomous, as a boom operator is no longer required. Operating beyond 100 GHz provides enough resolution and a narrow enough beamwidth that an approach corridor of centimeter scales can be maintained. Two modules were used to accomplish this task. The first module is a localizer/glide-slope module that can be fitted on a refueling aircraft. This module provides the navigation beams for aligning the approaching aircraft. The second module is navigational receiver fitted onto the approaching aircraft to be re fueled that can detect the approach beams. Since unmanned aircraft have a limited payload size and limited electrical power, the receiver portion was implemented in CMOS (complementary metal oxide semiconductor) technology based on a super-regenerative receiver (SRR) architecture. The SRR achieves mW-level power consumption and chip sizes less than l mm2. While super-regenerative techniques have small bandwidths that limit use in communication systems, their advantages of high sensitivity, low complexity, and low power make them ideal in this situation where modulating tones of less than 1 kHz are used.

Tang, Adrian J.↗
NASA NTRS2019

Analysis of At-Altitude LTE Power Spectra for C2 Communications for UAS Traffic Management

The National Aeronautics and Space Administration’s (NASA) Unmanned Aircraft Systems Traffic Management (UTM) project works to develop tools and technologies essential for safely enabling civilian low-altitude small Unmanned Aerial Systems (sUAS, also known as drones) operations. This paper presents results of work completed in the paper [1] presented at the 2018 ICNS conference where proposed approaches were explored for evaluating and analyzing sUAS Command and Control (C2) links based on commercial cellular networks. This paper focuses on the UTM Project’s Technology Capability Level 3 (TCL-3) test results which address the communications portion identified within the same paper. A software defined radio (SDR) was flown as a sUAS payload to capture received signal spectrum in Long Term Evolution (LTE) frequency bands of interest. The purpose was to measure the RF environment at UTM altitudes to characterize the interference potential. The SDR payload was flown at various stationary altitudes where the LTE over-the-air complex (I/Q) samples were captured by the SDR and later post-processed. The SDR received inputs through an omnidirectional antenna. The complex samples captured were an aggregate of transmissions received from all line-of-sight (LOS) towers within the geographic area for the specific radio frequency bandwidth the SDR is programmed to capture. Using this approach, the complex samples captured do not distinguish between the various eNodeB's (Long Term Evolution (LTE) transmitting towers). The complex samples were post processed via a Discrete Fourier Transform (DFT) algorithm to view the captured spectrum along with the power levels across the captured LTE bandwidth. This SDR payload process of capturing complex samples was done at two different regions within the US: 1) NASA's Ames Research Center (ARC) in Moffett Field, CA, and 2) Griffiss Airfield in Rome, NY. The data capture at the ARC site was done at two physical locations within the Ames campus where many stationary altitude captures where done as high as 800 ft. above ground level (AGL). The data captured at the Griffiss Airport (also known as the NY Corridor Site) were acquired at one location with three specific stationary altitude levels – {Ground Level (GL), 300 ft., and 400 ft.}. The LTE spectrum power levels were captured for two LTE carriers, AT&T and Verizon, at both sites where their respective spectra and power levels were measured and compared at various altitudes. The overall results show that there is an increase in LTE spectrum power levels at higher altitudes for drones. A detailed analysis of this data and conclusions drawn from the results are presented in this paper.

Kerczewski, Robert J.↗
NASA NTRS2006

A Comprehensive Assessment of Biologicals Contained Within Commercial Airliner Cabin Air

Both culture-based and culture-independent, biomarker-targeted microbial enumeration and identification technologies were employed to estimate total microbial and viral burden and diversity within the cabin air of commercial airliners. Samples from each of twenty flights spanning three commercial carriers were collected via air-impingement. When the total viable microbial population was estimated by assaying relative concentrations of the universal energy carrier ATP, values ranged from below detection limits (BDL) to 4.1 x 106 cells/cubic m of air. The total viable microbial population was extremely low in both of Airline A (approximately 10% samples) and C (approximately 18% samples) compared to the samples collected aboard flights on Airline A and B (approximately 70% samples). When samples were collected as a function of time over the course of flights, a gradual accumulation of microbes was observed from the time of passenger boarding through mid-flight, followed by a sharp decline in microbial abundance and viability from the initiation of descent through landing. It is concluded in this study that only 10% of the viable microbes of the cabin air were cultivable and suggested a need to employ state-of-the art molecular assay that measures both cultivable and viable-but-non-cultivable microbes. Among the cultivable bacteria, colonies of Acinetobacter sp. were by far the most profuse in Phase I, and Gram-positive bacteria of the genera Staphylococcus and Bacillus were the most abundant during Phase II. The isolation of the human pathogens Acinetobacter johnsonii, A. calcoaceticus, Janibacter melonis, Microbacterium trichotecenolyticum, Massilia timonae, Staphylococcus saprophyticus, Corynebacterium lipophiloflavum is concerning, as these bacteria can cause meningitis, septicemia, and a handful of sometimes fatal diseases and infections. Molecular microbial community analyses exhibited presence of the alpha-, beta-, gamma-, and delta- proteobacteria, as well as Gram-positive bacteria, Fusobacteria, Cyanobacteria, Deinococci, Bacterioidetes, Spirochetes, and Planctomyces in varying abundance. Neisseria meningitidis rDNA sequences were retrieved in great abundance from Airline A followed by Streptococcus oralis/mitis sequences. Pseudomonas synxantha sequences dominated Airline B clone libraries, followed by those of N. meningitidis and S. oralis/mitis. In Phase II, Airline C, sequences representative of more than 113 species, enveloping 12 classes of bacteria, were retrieved. Proteobacterial sequences were retrieved in greatest frequency (58% of all clone sequences), followed in short order by those stemming from Gram-positives bacteria (31% of all clone sequences). As for overall phylogenetic breadth, Gram-positive and alpha-proteobacteria seem to have a higher affinity for international flights, whereas beta-and gamma-proteobacteria are far more common about domestic cabin air parcels in Airline C samples. Ultimately, the majority of microbial species circulating throughout the cabin airs of commercial airliners are commensal, infrequently pathogenic normal flora of the human nasopharynx and respiratory system. Many of these microbes likely originate from the oral and nasal cavities, and lungs of passengers and flight crew and are disseminated unknowingly via routine conversation, coughing, sneezing, and stochastic passing of fomites. The data documented in this study will be useful to generate a baseline microbial population database and can be utilized to develop biosensor instrumentation for monitoring microbial quality of cabin or urban air.

microbial diversity↗