702 research outputs found

    Bayesian learning of models for estimating uncertainty in alert systems: application to air traffic conflict avoidance

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    Alert systems detect critical events which can happen in the short term. Uncertainties in data and in the models used for detection cause alert errors. In the case of air traffic control systems such as Short-Term Conflict Alert (STCA), uncertainty increases errors in alerts of separation loss. Statistical methods that are based on analytical assumptions can provide biased estimates of uncertainties. More accurate analysis can be achieved by using Bayesian Model Averaging, which provides estimates of the posterior probability distribution of a prediction. We propose a new approach to estimate the prediction uncertainty, which is based on observations that the uncertainty can be quantified by variance of predicted outcomes. In our approach, predictions for which variances of posterior probabilities are above a given threshold are assigned to be uncertain. To verify our approach we calculate a probability of alert based on the extrapolation of closest point of approach. Using Heathrow airport flight data we found that alerts are often generated under different conditions, variations in which lead to alert detection errors. Achieving 82.1% accuracy of modelling the STCA system, which is a necessary condition for evaluating the uncertainty in prediction, we found that the proposed method is capable of reducing the uncertain component. Comparison with a bootstrap aggregation method has demonstrated a significant reduction of uncertainty in predictions. Realistic estimates of uncertainties will open up new approaches to improving the performance of alert systems

    Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

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    Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations

    Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures

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    Realistic aircraft trajectory models are useful in the design and validation of air traffic management (ATM) systems. Models of aircraft operated under instrument flight rules (IFR) require capturing the variability inherent in how aircraft follow standard flight procedures. The variability in aircraft behavior varies among flight stages. In this paper, we propose a probabilistic model that can learn the variability from the procedural data and flight tracks collected from radar surveillance data. For each segment, a Gaussian mixture model is used to learn the deviations of aircraft trajectories from their procedures. Given new procedures, we can generate synthetic trajectories by sampling a series of deviations from the trained Gaussian distributions and reconstructing the aircraft trajectory using the deviations and the procedures. We extend this method to capture pairwise correlations between aircraft and show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft. We demonstrate the proposed models on the arrival tracks and procedures of the John F. Kennedy International Airport. The distributional similarity between the original and the synthetic trajectory dataset was evaluated using the Jensen-Shannon divergence between the empirical distributions of different variables. We also provide qualitative analyses of the synthetic trajectories generated from the models

    Learning Generative Models for Climbing Aircraft from Radar Data

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    Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspecification between predicted and observed trajectories. This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 66.3% less error when compared to BADA; generated trajectories that are realistic when compared to test data; and a means of computing confidence bounds for minimal computational cost

    Engage D5.6 Thematic challenge briefing notes (1st and 2nd releases)

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    Engage identified four thematic challenges to address research topics not contemporaneously (sufficiently) addressed by SESAR. This deliverable serves primarily as a record of the two sets of released thematic challenge briefing notes

    Existing and Required Modeling Capabilities for Evaluating ATM Systems and Concepts

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    ATM systems throughout the world are entering a period of major transition and change. The combination of important technological developments and of the globalization of the air transportation industry has necessitated a reexamination of some of the fundamental premises of existing Air Traffic Management (ATM) concepts. New ATM concepts have to be examined, concepts that may place more emphasis on: strategic traffic management; planning and control; partial decentralization of decision-making; and added reliance on the aircraft to carry out strategic ATM plans, with ground controllers confined primarily to a monitoring and supervisory role. 'Free Flight' is a case in point. In order to study, evaluate and validate such new concepts, the ATM community will have to rely heavily on models and computer-based tools/utilities, covering a wide range of issues and metrics related to safety, capacity and efficiency. The state of the art in such modeling support is adequate in some respects, but clearly deficient in others. It is the objective of this study to assist in: (1) assessing the strengths and weaknesses of existing fast-time models and tools for the study of ATM systems and concepts and (2) identifying and prioritizing the requirements for the development of additional modeling capabilities in the near future. A three-stage process has been followed to this purpose: 1. Through the analysis of two case studies involving future ATM system scenarios, as well as through expert assessment, modeling capabilities and supporting tools needed for testing and validating future ATM systems and concepts were identified and described. 2. Existing fast-time ATM models and support tools were reviewed and assessed with regard to the degree to which they offer the capabilities identified under Step 1. 3 . The findings of 1 and 2 were combined to draw conclusions about (1) the best capabilities currently existing, (2) the types of concept testing and validation that can be carried out reliably with such existing capabilities and (3) the currently unavailable modeling capabilities that should receive high priority for near-term research and development. It should be emphasized that the study is concerned only with the class of 'fast time' analytical and simulation models. 'Real time' models, that typically involve humans-in-the-loop, comprise another extensive class which is not addressed in this report. However, the relationship between some of the fast-time models reviewed and a few well-known real-time models is identified in several parts of this report and the potential benefits from the combined use of these two classes of models-a very important subject-are discussed in chapters 4 and 7

    Multi-sensor data fusion techniques for RPAS detect, track and avoid

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    Accurate and robust tracking of objects is of growing interest amongst the computer vision scientific community. The ability of a multi-sensor system to detect and track objects, and accurately predict their future trajectory is critical in the context of mission- and safety-critical applications. Remotely Piloted Aircraft System (RPAS) are currently not equipped to routinely access all classes of airspace since certified Detect-and-Avoid (DAA) systems are yet to be developed. Such capabilities can be achieved by incorporating both cooperative and non-cooperative DAA functions, as well as providing enhanced communications, navigation and surveillance (CNS) services. DAA is highly dependent on the performance of CNS systems for Detection, Tacking and avoiding (DTA) tasks and maneuvers. In order to perform an effective detection of objects, a number of high performance, reliable and accurate avionics sensors and systems are adopted including non-cooperative sensors (visual and thermal cameras, Laser radar (LIDAR) and acoustic sensors) and cooperative systems (Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS)). In this paper the sensors and system information candidates are fully exploited in a Multi-Sensor Data Fusion (MSDF) architecture. An Unscented Kalman Filter (UKF) and a more advanced Particle Filter (PF) are adopted to estimate the state vector of the objects based for maneuvering and non-maneuvering DTA tasks. Furthermore, an artificial neural network is conceptualised/adopted to exploit the use of statistical learning methods, which acts to combined information obtained from the UKF and PF. After describing the MSDF architecture, the key mathematical models for data fusion are presented. Conceptual studies are carried out on visual and thermal image fusion architectures
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