Applicability and Surrogacy of Uncorrelated Airspace Encounter Models at Low Altitudes

Underhill, Ngaire; Weinert, Andrew

doi:10.2514/1.d0254

Cited by 5 publications

(8 citation statements)

References 6 publications

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“…Similarly, approximately 4% and 6% of Open-Sky Network-based data were rotorcraft for Fort Lauderdale-Hollywood (FLL) and Addison (ADS), yet 12% and 15% were rotorcraft for BED and Sacramento (SMF). The tendency to observe rotorcraft operating at lower altitudes has also been observed when training the uncorrelated encounter models [7]. Due to the distance away from the terminal radars, it was expected that neither airport would have any processed tracks from that dataset.…”

Section: Initial Spatial Filteringmentioning

confidence: 78%

“…Principally, aircraft type of fixed-wing or rotorcraft can be identified when training with the OpenSky Network-based model but not when using the terminal area radars. For the uncorrelated encounter models, we have observed models trained solely using observations of rotorcraft will have a speed distribution slower than models trained using solely fixed-wing aircraft [7]. An uncorrelated rotorcraft-based model also has a relatively slower speed distribution than a model trained using heterogenous mix of aircraft types.…”

Section: Kinematic Distributions At Cpamentioning

confidence: 99%

“…These are trained on real-world observations of individual aircraft or observations of encounters between two aircraft. The majority of these models are uncorrelated [4][5][6][7], which assume that the aircraft are not participating in the air traffic control system and their behavior is independent of other aircraft. Conversely, a correlated model assumes that aircraft behavior and the relatively geometry between aircraft was dependent upon an air traffic service.…”

Section: Motivationmentioning

confidence: 99%

“…Intruder type: The type of aircraft of the intruder: can either be fixed-wing or rotorcraft. Note that while the OpenSky Network-based uncorrelated models are individually organized by aircraft type [7], aircraft type is an explicit model variable here.…”

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confidence: 99%

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Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

et al. 2022

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The integration of new airspace entrants into terminal operations requires design and evaluation of Detect and Avoid systems that prevent loss of well clear from and collision with other aircraft. Prior to standardization or deployment, an analysis of the safety performance of those systems is required. This type of analysis has typically been conducted by Monte Carlo simulation with synthetic, statistically representative encounters between aircraft drawn from an appropriate encounter model. While existing encounter models include terminal airspace classes, none explicitly represents the structure expected while engaged in terminal operations, e.g., aircraft in a traffic pattern. The work described herein is an initial model of such operations where an aircraft landing or taking off via a straight trajectory encounters another aircraft landing or taking off, or transiting by any means. The model shares the Bayesian network foundation of other Massachusetts Institute of Technology Lincoln Laboratory encounter models but tailors those networks to address structured terminal operations, i.e., correlations between trajectories and the airfield and each other. This initial model release is intended to elicit feedback from the standards-writing community.

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Section: Initial Spatial Filteringmentioning

confidence: 78%

Section: Kinematic Distributions At Cpamentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

mentioning

confidence: 99%

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Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

et al. 2022

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“…Sample-based techniques can be used to validate given sensing architectures and to estimate performance sensitivity to given parameters. While progress is being made in the development of encounter models at very low altitudes [18], it is clear that the absence of historical datasets poses challenges for sample-based approaches, also because scenarios and procedures involving UAM aircraft are still being defined in detail.…”

Section: Introductionmentioning

confidence: 99%

Analytical Framework for Sensing Requirements Definition in Non-Cooperative UAS Sense and Avoid

Fasano,

Opromolla

2023

Drones

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This paper provides an analytical framework to address the definition of sensing requirements in non-cooperative UAS sense and avoid. The generality of the approach makes it useful for the exploration of sensor design and selection trade-offs, for the definition of tailored and adaptive sensing strategies, and for the evaluation of the potential of given sensing architectures, also concerning their interface to airspace rules and traffic characteristics. The framework comprises a set of analytical relations covering the following technical aspects: field of view and surveillance rate requirements in azimuth and elevation; the link between sensing accuracy and closest point of approach estimates, expressed though approximated derivatives valid in near-collision conditions; the diverse (but interconnected) effects of sensing accuracy and detection range on the probabilities of missed and false conflict detections. A key idea consists of focusing on a specific target time to closest point of approach at obstacle declaration as the key driver for sensing system design and tuning, which allows accounting for the variability of conflict conditions within the aircraft field of regard. Numerical analyses complement the analytical developments to demonstrate their statistical consistency and to show quantitative examples of the variation of sensing performance as a function of the conflict geometry, as well as highlighting potential implications of the derived concepts. The developed framework can potentially be used to support holistic approaches and evaluations in different scenarios, including the very low-altitude urban airspace.

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Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling

Weinert¹,

Brittain²,

Underhill³

et al. 2021

2021 IEEE High Performance Extreme Computing Conference (HPEC)

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This document is derived from work done for the FAA (and possibly others), it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone's use of the information. The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages. The Federal Aviation Administration shall not be liable to anyone for any

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Applicability and Surrogacy of Uncorrelated Airspace Encounter Models at Low Altitudes

Abstract: to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. *Associate Staff, Surveillance Systems. Member AIAA.

Cited by 5 publications

References 6 publications

Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

Analytical Framework for Sensing Requirements Definition in Non-Cooperative UAS Sense and Avoid

Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling

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