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In recent years, there has been a rapid growth in applying machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection ...

To enable sustainable aviation growth, mitigation of environmental effects must be developed in parallel. To further this effort, these effects are modeled using capabilities such as the Aviation Environmental Design Tool (AEDT), a program that is able to model aircraft performance, fuel burn, emissions, and noise. Past and current projects are performed with the intent of improving the accuracy of the models within AEDT to capture various real-world effects. This paper targets the sensitivity of the noise prediction and propagation by varying multiple assumptions within AEDT. To validate the noise capabilities, multiple streams of real-world data will be used to accurately model actual flights to and from SFO airport. This data includes High-Fidelity weather data,detailed flight performance characteristics from airline flight data records and noise monitoring data obtained from stations around the airport. The results from this study are expected to offer recommendations and help users prioritize and more accurately quantify community noise exposure using AEDT.

Successful mitigation of aviation noise is a key enabler for sustainable aviation growth. A key focus of this effort is the noise arising from aircraft arrival operations. Arrival operations are characterized by the use of high-lift devices, deployment of landing gear, and low thrust levels, which results in the airframe being the major component of noise. In order to optimize for arrival noise, management of the flap schedule and gear deployment is crucial. This research aims to create an optimization framework for evaluating various aircraft trajectories in terms of their noise impact. A parametric representation of the aircraft arrival trajectory will be created to allow for the variation of aircraft's flap schedule. The Federal Aviation Administration's Aviation Environmental Design Tool will be used to simulate the aircraft trajectory and performance, and to compute the noise metrics. Specifically, the latest performance model from EUROCONTROL called "Base of Aircraft Data - Family 4" will be used. This performance model contains higher fidelity modeling of aircraft aerodynamics and other characteristics which allows for better parametric variation.

Ground delay programs and ground stops are implemented to ensure that airport and flight operations remain safe in spite of constraint(s) at affected airports. Occasionally, ground stops are issued...

This paper demonstrates the development of purely data-driven, nonintrusive parametric reduced-order models for the emulation of high-dimensional field outputs using randomized linear algebra techn...

Performance models facilitate a wide range of safety analyses in aviation. In an ideal scenario, the performance models would show inherently good agreement with the true performance of the aircraf...

This paper presents the application of machine learning and open-source data to improve the prediction capability of conflict between aircraft in terminal airspace. An analytical methodology is developed that synthesizes trajectory clustering, classification-based supervised learning, and probabilistic modeling by leveraging publicly available flight data to streamline the probability of conflict and to facilitate the early detection of potential threat in terminal airspace. As such, application of this methodology yields an early detection of potential threats and allows for their resolution ahead of time. The methodology developed outlines a robust and repeatable framework that is applicable across heterogeneous data sets containing multiple aircraft and airport of operations. This framework is implemented on landing data at Zurich airport. The data are retrieved from the OpenSky Network which provides air traffic surveillance data from ADS-B messages. The work concludes by demonstrating the capability of the algorithm on a use case and provides initial guidance for its integration with existing collision avoidance systems.

The maintenance and improvement of safety are among the most critical concerns in civil aviation operations. Due to the increased availability of data and improvements in computing power, applying artificial intelligence technologies to reduce risk in aviation safety has gained momentum. In this paper, a framework is developed to build a predictive model of future aircraft trajectory that can be utilized online to assist air crews in their decision-making during approach. Flight data parameters from the approach phase between certain approach altitudes (also called gates) are utilized for training an offline model that predicts the aircraft’s ground speed at future points. This model is developed by combining convolutional neural networks (CNNs) and long short-term memory (LSTM) layers. Due to the myriad of model combinations possible, hyperband algorithm is used to automate the hyperparameter tuning process to choose the best possible model. The validated offline model can then be used to predict the aircraft’s future states and provide decision-support to air crews. The method is demonstrated using publicly available Flight Operations Quality Assurance (FOQA) data from the National Aeronautics and Space Administration (NASA). The developed model can predict the ground speed at an accuracy between 1.27% and 2.69% relative root-mean-square error. A safety score is also evaluated considering the upper and lower bounds of variation observed within the available data set. Thus, the developed model represents an improved performance over existing techniques in literature and shows significant promise for decision-support in aviation operations.

As air traffic demand grows, robust, data-driven anomaly detection methods are required to ensure that aviation systems become safer and more efficient. The terminal airspace is identified as the most critical airspace for both individual flight-level and system-level safety and efficiency. As such, developing data-driven anomaly detection methods to analyze terminal airspace operations is paramount. With the expansion of ADS-B technology, open-source flight tracking data has become more readily available to enable larger-scale analyses of aircraft operations. This paper makes a distinction between spatial metrics in ADS-B trajectory data and energy metrics derived from ADS-B trajectory data. Motivated by the limited number of approaches that simultaneously consider both spatial and energy metrics, this paper introduces the concepts of spatial anomalies and energy anomalies. In particular, it proposes a novel, unified framework for detection of spatial and energy anomalies in ADS-B trajectory data (and associated derived metrics). The framework consists of three main parts - a data processing procedure, a spatial anomaly detection method, and an energy anomaly detection method. The framework is demonstrated utilizing four months of ADS-B trajectory data associated with arrivals at San Francisco International Airport, and the relationship between the spatial and energy anomalies in this terminal airspace is explored. The results that stem from the implementation of this framework indicate that if an aircraft is spatially not conforming to an identified set of air traffic flows representing standard spatial operations, then this aircraft is more likely to experience non-conformance to standard operations in its energy metrics. Aviation operators, such as air traffic controllers, may benefit from this observation, as it may factor into decision-making in instances where there is the potential to instruct an aircraft to spatially deviate from standard operations. Additionally, this research revealed underlying differences between trajectories that are spatially nominal yet energy-anomalous and those trajectories that are spatially anomalous and energy-anomalous. Focusing solely on energy anomaly detection does not provide insight into potential spatial-related decisions that may have been made to result in off-nominal energy behavior.

Unmanned Aircraft Systems (UAS) have become a regular part of military operations and civilian life since the early 2000s. The proliferation of drone technologies has led to reduced costs and increased accessibility for individuals, states, and non-state actors. While extensive research and commentary have been made on the impacts of high-end military drones, the literature is comparatively sparse on the impacts of small UAS. With the regulations for civilian drones set to be finalized shortly, the potential impacts of small UAS on safety, privacy, and security need to be understood to have the right regulatory framework in place. This chapter deals with estimating the projected impacts of small UAS proliferation and increased autonomy on security. A robust analysis is presented by evaluating the disruptive potential of national security in various scenarios by analyzing the current and future trends of drone technologies, regulations, threats, and other important variables. The framework provided lays the groundwork for further studies into the impact of small UAS on security and hopes to inform future usage, perception, and policy surrounding these systems.

International audience; In recent years, multi-layered hierarchical compositions of the well-known and widely used Gaussian process models called deep Gaussian processes are finding use in the approximation of black-box functions. In this paper, the performance of deep Gaussian process models is empirically evaluated and compared against the well-established Gaussian process models with a special emphasis on engineering problems. The work draws conclusions through detailed comparisons in terms of metrics such as computational training cost, data requirement, predictive error, and robustness to the choice of the initial design of experiments. Additionally, the viability and robustness of Deep Gaussian process models for applications on practical engineering problems are analyzed through sensitivity to hyperparameters and scalability with respect to the input space dimensionality respectively. Finally, the models are also compared in an adaptive construction setting, where they are built sequentially by selecting points that maximize posterior variance. Experiments are conducted on canonical test functions with varying input dimensions, an engineering test function, and a practical transonic airfoil test case with a high-dimensional input space. The experiments suggest that deep Gaussian process models outperform traditional Gaussian process models in terms of accuracy at the cost of incurring a significantly higher computational expense for the training procedure. The sensitivity studies indicate that inducing points is the most important hyperparameter that affects deep Gaussian process performance and training time. This work empirically shows that deep Gaussian processes are promising candidates for problems that are known to be nonlinear, high-dimensional, and when limited training data is available.

International audience; Quantification and improvement of safety is one of the most important objectives among the General Aviation community. In recent years, machine learning techniques have emerged as an important enabler in the data-driven safety enhancement of aviation operations with a number of techniques being applied to flight data to identify and isolate anomalous (and potentially unsafe) operations. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a heterogeneous fleet of aircraft and operations. In this paper, a novel method of identifying instantaneous anomalies for retrospective safety analysis in General Aviation using energy-based metrics is proposed. Each flight data record is processed by a sliding window across the multi-variate time series of evaluated metrics. A Gaussian Mixture Model using energy metrics and their variability within each window is fit in order to predict the probability of any instant during the flight being nominal. Instances during flights that deviate from the nominal are isolated to identify potential increased levels of risk. The identified anomalies are compared with traditional methods of safety assessment such as exceedance detection to highlight the benefits of the developed method. The methodology is demonstrated using flight data records from two representative aircraft for critical phases of flight.

As air traffic demand grows, robust, data-driven methods are required to ensure that aviation systems become safer and more efficient. The terminal airspace is identified as the most critical airspace for both individual flight-level and system-level safety and efficiency. As such, developing data-driven anomaly detection methods to analyze terminal airspace operations has been an active area of research. With the expansion of ADS-B technology, open-source flight tracking data has become more readily available to enable larger-scale analyses of aircraft operations. Generally, the methods developed to detect anomalies in ADS-B trajectory data detect anomalies in either the spatial or energy dimension. These methods distinctly use either spatial metrics or energy metrics, derived from ADS-B trajectory data. Motivated by the limited number of approaches that simultaneously consider both spatial and energy metrics, this paper introduces the concepts of spatial anomalies and energy anomalies and performs a novel investigation into the relationships and interdependencies, if any exist, between the two types of anomalies. To enable this analysis, spatial and energy anomalies are detected in ADS-B trajectory data (and associated derived metrics) using HDBSAN and DBSCAN clustering algorithms, respectively. Four months of ADS-B trajectory data associated with arrivals at San Francisco International Airport is extracted, cleaned and processed. The results that stem from this investigation indicate that if an aircraft is not spatially conforming to an identified set of air traffic flows representing standard spatial operations, then this aircraft is more likely to experience non-conformance to standard operations in its energy metrics. Additionally, this research reveals underlying differences between trajectories that are spatially nominal and energy-anomalous and those trajectories that are spatially anomalous and energy-anomalous. Focusing solely on energy anomalies does not provide insight into potential spatial-related decisions that may have been made to result in off-nominal energy behavior.

Energy management and energy state awareness are important concepts in aircraft safety analysis. Many loss-of-control accidents are correlated to poor energy management. Energy-based metrics provid...

In recent years, due to the increased availability of data and improvements in computing power, application of machine learning techniques to various aviation safety problems for identifying, isolating, and reducing risk has gained momentum. Data collected from on-board recorders in commercial aircraft contain thousands of parameters in the form of multivariate time-series (continuous, discrete, categorical, etc.) which are used to train the machine learning models. Among the phases of flight, approach and landing phases result in the most accidents and incidents. The performance and trajectory of the aircraft during the approach phase is an indicator of its landing performance which, in turn, affects incident or accident probability such as runway excursions. Landing performance is commonly measured using metrics such as landing airspeed, vertical speed, location of touchdown point on runway, etc. While current applications of machine learning to aviation focus on retrospective insights to implement corrective measures, they offer limited value for real-time risk identification or decision-making as they are inherently reactive in nature. In this work, a novel offline-online framework is developed for building a global predictive model offline to predict landing performance metrics online. The framework leverages flight data from the approach phase between certain approach altitudes (also called gates) in order to train the offline model to predict the landing true airspeed and ground speed using a Random Forest regression algorithm. Permutation importance is used to identify and retain important features among those available and the training data is balanced with respect to flight safety events to ensure a good performing model. The developed global model is robust and can predict landing true airspeed and ground speed with root mean square errors of 2.62 and 2.98 knots respectively over six different airframes operating at over seventy airports. The model operates fast in prediction mode which, coupled with the ability to provide the prediction at an altitude where a go-around decision may be made, makes it particularly suitable for online application. The framework is demonstrated using data obtained from commercial airline operations that contains thousands of flight records and performs better than existing techniques in literature at predicting true airspeed and ground speed at touchdown.