Airspace Encounter Models Overview

For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. Encounter models represent the encounter geometry, but also the aircraft behavior (accelerations) during the course of the encounter. Encounter models have been developed for many different manned operational contexts, but there are new considerations for unmanned aircraft operations including the lower operating altitudes and interactions with service providers and other users.

This GitHub organization and associated repositories are intended to eventually supersede the legacy MIT Lincoln Laboratory hosted encounter model website and align encounter model development with modern community driven software practices. As of 2019, this original MIT Lincoln Laboratory-hosted website has not been deprecated by this GitHub organization yet. Not all encounter model content is available on GitHub. Note that the legacy website is currently down for extended maintenance.

• Airspace Encounter Models Overview
  • Point of Contact
  • Nomenclature
  • Sponsors
    • UAS ExCom Science and Research Panel
  • Encounter Model Requirements
  • Documentation
    • Introductory
    • Manned Correlated Model
    • Manned Due Regard
    • Manned Helicopter Air Ambulance Model
    • Manned Littoral Model
    • Manned Uncorrelated-Conventional Models
    • Manned Uncorrelated-Unconventional Model
    • Unmanned Uncorrelated Model
    • Unmanned Recreational Model
    • Urban Air Mobility Uncorrelated Model
    • Terminal Encounter Model
  • Encounter Categories
    • Manned vs Manned
    • Unmanned vs Manned
    • Unmanned vs Unmanned
  • Download
    • Terminology
    • Trajectories
    • Encounter Sets
  • Select Applications and Use Cases
    • Historical Perspective
    • Well Clear for UAS
    • Evaluation of TCAS II Version 7.1
  • Distribution Statement

Point of Contact

We encourage the use of the GitHub Issues but when email is required, please contact the administrators at [email protected]. As the encounter models transition to a more community driven effort, a separate mailing list for code discussion may be created.

Nomenclature

Sponsors

Development has been funded by a variety of sponsors, primarily U.S. federal agencies, to support many different aviation safety efforts. This section is reserved for overviews of key funding agencies and the development they supported. It is the sponsor's discretion to provide content for this section.

UAS ExCom Science and Research Panel

Since 2015, encounter model development has been funded by the UAS Executive Committee (ExCom) SARP, an organization chartered under the ExCom Senior Steering Group (SSG). The SARP consists of senior managers in eight U.S. Government federal agencies – DoD, FAA, NASA, DHS, DOI, DOC, DOJ, and DOE. It was founded in 2011 by the Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics for Unmanned Warfare; in 2013, it was realigned and expanded under the UAS ExCom SSG. The SARP is led by a U.S. Government senior research advisor from the FAA and two co-chairs appointed by the SSG.

The SARP’s process involves collaboratively identifying key research needs, gaps, and overlaps through maintenance of a prioritized “gap” list. The SARP has four primary goals:

• To focus the scientific and technical capabilities of a broad Federal technical community upon key UAS integration science and research initiatives,
• To provide insight as to how these initiatives may align with commercial and academic science and research efforts to avoid duplication and reduce cost,
• To identify and validate (through the ExCom) known research gaps that impact the airspace integration of UAS and to review available published studies, and
• To coordinate and lead interagency resources and expertise to develop specific solutions and/or recommendations that address the ExCom’s highest-priority research gaps for UAS airspace integration.

As of 2015, the SARP addressed primary gaps of sUAS versus manned aircraft well clear, UAS risk to people on the ground, and barriers to multiple UAS operations. To support the SARP recommendations to the ExCom SSG, encounter models of unmanned aircraft behavior and low altitude airspace were required and developed.

Encounter Model Requirements

With the exception of the uncorrelated UAS model, each encounter model of manned aircraft is a Bayesian Network, a representation of a multivariate probability distribution as a directed acyclic graph and trained using aircraft operational data derived from radar or other sensing system flight track data. With the exception of the unmanned recreational model and urban air mobility uncorrelated model which were developed in collaboration with Stanford University, all these models were originally developed by MIT Lincoln Laboratory.

To enable assessment of aviation safety systems such as collision avoidance or detect and avoid systems, these models are designed with the following requirements:

• Be built using aircraft operational data;
• Have dependence on geographic region, airspace class, and altitude layer;
• Have a representative time period for different evaluations;
• Reflect realistic aircraft flight dynamics;
• Realistically capture relative aircraft geometry; and
• Enable fast-time simulation.

Documentation

The encounter models are described in the brief introductory material that follows as well as in comprehensive technical reports.

Generally speaking, there are two types of encounter model: correlated and uncorrelated, where correlation is defined as a statistical dependence between conflicting aircraft due to factors before a collision avoidance system like TCAS or a UAS DAA system acts, modeling at least 60 seconds prior to a closet point of approach. If a sufficient correlation exists, then it must be modeled. One of the main causes of correlation in encounters that must be accounted for in the models is air traffic control; however, air traffic control is not directly applicable to all UAS operational concepts. It is possible that NASA prototype UTM separation services may induce a similar correlation, provided the conflict is made aware to UTM. However as of October 2019, since there are currently no accepted or widely operational UTM services, encounters with sUAS are assumed to be uncorrelated.

Citations are listed in descending chronological order, which the newest entries listed first. Clicking the arrow next the citation will display the Bibtex entry for each citation. Each Bibtex entry includes a URL to access the citation. All citations can also be found in the Zotero group, airspace-encounter-model.

Introductory

@article{kochenderferAirspaceEncounterModels2010,
	title = {Airspace {Encounter} {Models} for {Estimating} {Collision} {Risk}},
	url = {https://doi.org/10.2514/1.44867},
	volume = {33},
	doi = {10.2514/1.44867},
	number = {2},
	journal = {Journal of Guidance, Control, and Dynamics},
	author = {Kochenderfer, Mykel J. and Edwards, Matthew W. M. and Espindle, Leo P. and Kuchar, James K. and Griffith, J. Daniel},
	month = apr,
	year = {2010},
	pages = {487--499}
}

@article{kochendedrferComprehensiveAircraftEncounter2008,
	title = {A {Comprehensive} {Aircraft} {Encounter} {Model} of the {National} {Airspace} {System}},
	volume = {17},
	url = {https://pdfs.semanticscholar.org/4086/9e6358bded8c07a7e5480facee4223fa0a29.pdf},
	language = {en},
	number = {2},
	journal = {Lincoln Laboratory Journal},
	author = {Kochendedrfer, Mykel J. and Espindle, Leo P. and Kuchar, James K. and Griffith, J. Daniel},
	year = {2008},
	pages = {41--53}
}

Manned Correlated Model

A Bayesian network used to generate random close encounters between transponder-equipped (cooperative) aircraft. This is the one of the few models that explicitly models two aircraft.

Repository: em-model-manned-bayes

@techreport{underhillCorrelatedEncounterModel2018,
  title = {Correlated {{Encounter Model}} for {{Cooperative Aircraft}} in the {{National Airspace System}}; {{Version}} 2.0},
  url = {https://apps.dtic.mil/docs/citations/AD1051496},
  author = {Underhill, N.K and Harkleroad, E.P and Guendel, R.E and Weinert, A.J and Maki, D.E and Edwards, M.W.M},
  type = {Project {{Report}}},
  language = {en},
  number = {ATC-440},
  institution = {{Massachusetts Institute of Technology, Lincoln Laboratory}},
  month = may,
  year = {2018},
  pages = {140}
}

@techreport{kochenderferCorrelatedEncounterModel2008,
	title = {Correlated {Encounter} {Model} for {Cooperative} {Aircraft} in the {National} {Airspace} {System}},
	url = {https://www.ll.mit.edu/r-d/publications/correlated-encounter-model-cooperative-aircraft-national-airspace-system-version},
	type = {Project {Report}},
	number = {ATC-344},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Kochenderfer, M. J. and Espindle, Leo P. and Kuchar, James K. and Griffith, J. D.},
	year = {2008}
}

Manned Due Regard

A Bayesian network trained using using the enhanced Traffic Management System (ETMS) data feed that was provided by the Volpe Center to describe aircraft operating in international airspace.

Repository: em-model-manned-bayes

@techreport{griffithDueRegardEncounter2013,
	title = {Due {Regard} {Encounter} {Model} {Version} 1.0},
	address = {Lexington, MA},
	type = {Project {Report}},
	number = {ATC-397},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Griffith, John D. and Edwards, Matthew W. and Miraflor, Raymond M. and Weinert, Andrew J.},
	month = aug,
	year = {2013},
	url = {https://apps.dtic.mil/docs/citations/ADA589692},
	pages = {56}
}

Manned Helicopter Air Ambulance Model

A Bayesian network trained using flight operational quality assurance (FOQA) data provided by a Massachusetts-based HAA provider.

Note a technical description of this model has not been publicly released yet. This model was developed to support sUAS well clear research. Until model-specific documentation is released, please cite the following paper.

@article{weinertWellClearRecommendationSmall2018,
	title = {Well-{Clear} {Recommendation} for {Small} {Unmanned} {Aircraft} {Systems} {Based} on {Unmitigated} {Collision} {Risk}},
	volume = {26},
	url = {https://doi.org/10.2514/1.D0091},
	doi = {10.2514/1.D0091},
	number = {3},
	urldate = {2019-01-09},
	journal = {Journal of Air Transportation},
	author = {Weinert, Andrew and Campbell, Scot and Vela, Adan and Schuldt, Dieter and Kurucar, Joel},
	year = {2018},
	pages = {113--122}
}

Manned Littoral Model

A Bayesian network model that describes how aircraft behavior in the littoral regions of the United States. This model has been deprecated by the manned uncorrelated model 2.0. Note that Appendix C in CASSATT-2 describes a revised process from the early encounter models for initializing uncorrelated encounters and estimating metrics.

Repository: em-model-manned-bayes

@techreport{edwardsEncounterModelsLittoral2010,
	title = {Encounter {Models} for the {Littoral} {Regions} of the {National} {Airspace} {System}},
	url = {https://apps.dtic.mil/docs/citations/ADA529083},
	language = {en},
	number = {CASSATT-2},
	urldate = {2019-01-16},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Edwards, Matthew W.},
	month = sep,
	year = {2010}
}

Manned Uncorrelated-Conventional Models

A Bayesian network model that assume the aircraft trajectories are independent and that there is no dependence on aircraft behavior before a collision avoidance system acts. It is assumed that air traffic control or an active unmanned traffic management system is not providing guidance. This uncorrelated assumption enables individual aircraft trajectories can be modeled independently and then combined in a separate encounter initialization process

Repository: em-model-manned-bayes

@article{underhillApplicabilitySurrogacyUncorrelated2021,
  title = {Applicability and {{Surrogacy}} of {{Uncorrelated Airspace Encounter Models}} at {{Low Altitudes}}},
  author = {Underhill, Ngaire and Weinert, Andrew},
  year = {2021},
  month = mar,
  pages = {8},
  archiveprefix = {arXiv},
  eprint = {2103.04753},
  eprinttype = {arxiv},
  journal = {arXiv:2103.04753 [cs, eess]},
  keywords = {Computer Science - Machine Learning,Electrical Engineering and Systems Science - Systems and Control},
  primaryclass = {cs, eess}
}

@article{weinertProcessingCrowdsourcedObservations2020,
  url = {http://arxiv.org/abs/2008.00861},
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {2008.00861},
  primaryClass = {cs},
  title = {Processing of {{Crowdsourced Observations}} of {{Aircraft}} in a {{High Performance Computing Environment}}},
  author = {Weinert, Andrew and Underhill, Ngaire and Gill, Bilal and Wicks, Ashley},
  month = aug,
  year = {2020},
  keywords = {Computer Science - Computational Engineering; Finance; and Science,Computer Science - Distributed; Parallel; and Cluster Computing,E.2,H.3,I.6.5}
}

@inproceedings{weinertDevelopingLowAltitude2019,
	title = {Developing a {Low} {Altitude} {Manned} {Encounter} {Model} {Using} {ADS}-{B} {Observations}},
	url = {https://doi.org/10.1109/AERO.2019.8741848},
	doi = {10.1109/AERO.2019.8741848},
	address = {Big Sky, MT},
	language = {en},
	booktitle = {2019 {IEEE} {Aerospace} {Conference}},
	author = {Weinert, Andrew and Underhill, Ngaire and Wicks, Ashley},
	month = mar,
	year = {2019},
	pages = {1--8}
}

@techreport{weinertUncorrelatedEncounterModel2013,
	address = {Lexington, MA},
	type = {Project {Report}},
	title = {Uncorrelated {Encounter} {Model} of the {National} {Airspace} {System} {Version} 2.0},
	copyright = {All rights reserved},
	number = {ATC-404},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Weinert, Andrew J. and Harkleroad, Eric P. and Griffith, John D. and Edwards, Matthew W. and Kochenderfer, Mykel J.},
	month = aug,
	url = {https://apps.dtic.mil/docs/citations/ADA589697},
	year = {2013},
	pages = {93}
}

@inproceedings{weinertExtendedAirspaceEncounter2013,
	address = {Boston, MA},
	title = {Extended {Airspace} {Encounter} {Models} for {Unmanned} {Aircraft} {Sense} and {Avoid} {Safety} {Evaluation}},
	url = {http://arc.aiaa.org/doi/abs/10.2514/6.2013-5049},
	doi = {10.2514/6.2013-5049},
	urldate = {2013-08-23},
	booktitle = {{AIAA} {Infotech}@{Aerospace} ({I}@{A}) {Conference}},
	publisher = {American Institute of Aeronautics and Astronautics},
	author = {Weinert, Andrew J. and Harkleroad, Eric and Griffith, John and Edwards, Matthew W. and Chen, Christine C.},
	month = aug,
	year = {2013}
}

@techreport{kochenderferUncorrelatedEncounterModel2008,
	type = {Project {Report}},
	title = {Uncorrelated {Encounter} {Model} of the {National} {Airspace} {System} version 1.0},
	number = {ATC-345},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Kochenderfer, M. J. and Kuchar, J. K. and Espindle, L. P. and Griffith, J. D.},
	url = {https://www.ll.mit.edu/r-d/publications/uncorrelated-encounter-model-national-airspace-system-version-10},
	year = {2008}
}

Manned Uncorrelated-Unconventional Model

A set of nine individual Bayesian network models encompassing ultralights, gliders, balloons, and airships. This model is based on more than 96,000 unconventional aircraft tracks.

Repository: em-model-manned-bayes

@techreport{edwardsEncounterModelsUnconventional2009,
	type = {Project {Report}},
	title = {Encounter {Models} for {Unconventional} {Aircraft}, {Version} 1.0},
	number = {ATC-348},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Edwards, Matthew W. and Kochendedrfer, Mykel J. and Kuchar, James K. and Espindle, Leo P.},
	url = {https://www.ll.mit.edu/r-d/publications/encounter-models-unconventional-aircraft-version-10},
	year = {2009}
}

Unmanned Uncorrelated Model

This discriminative model takes into account the operational intent of UAS commercial operations and generates trajectories based on open source maps of infrastructure, recreational regions, and other common sUAS surveillance targets. This model is not a generative Bayesian Network like the others.

This model is applicable for some commercial operations, such as long linear infrastructure inspection, governed by 14 CFR Part 107 (sUAS rule) or 14 CFR Part 135 (air carrier). It is not applicable for sUAS recreational and amateur operations governed by 14 CFR Part 101 or 49 U.S.C. 44809.

@inproceedings{weinertRepresentativeSmallUAS2020,
  url = {https://doi.org/10.2514/6.2020-0741},
  address = {{Orlando, FL}},
  title = {Representative {{Small UAS Trajectories}} for {{Encounter Modeling}}},
  booktitle = {{{AIAA Scitech}} 2020 {{Forum}}},
  publisher = {{American Institute of Aeronautics and Astronautics}},
  doi = {10.2514/6.2020-0741},
  author = {Weinert, Andrew J. and Edwards, Matthew and Alvarez, Luis and Katz, Sydney Michelle},
  month = jan,
  year = {2020}
}

@inproceedings{weinertGeneratingRepresentativeSmall2018,
	title = {Generating {Representative} {Small} {UAS} {Trajectories} using {Open} {Source} {Data}},
	url = {https://ieeexplore.ieee.org/document/8569745},
	doi = {10.1109/DASC.2018.8569745},
	booktitle = {2018 {IEEE}/{AIAA} 37th {Digital} {Avionics} {Systems} {Conference} ({DASC})},
	author = {Weinert, Andrew and Underhill, Ngaire},
	month = sep,
	year = {2018},
	keywords = {Aircraft, Atmospheric modeling, FAA, Monte Carlo methods, Standards, Surveillance, Trajectory},
	pages = {1--10}
}

Unmanned Recreational Model

A Bayesian network model trained on data from DroneShare, which was a website in which hobbyists could upload their telemetry log files. DroneShare is no longer active but data for download by the public was previously avaiable for over 75,000 flights.

@inproceedings{mueller2016simulation,
  title={Simulation Comparison of Collision Avoidance Algorithms for Small Multi-Rotor Aircraft},
  author={Mueller, Eric R and Kochenderfer, Mykel J},
  booktitle={AIAA Modeling and Simulation Technologies Conference},
  pages={3674},
  year={2016}
}

@PhdThesis{Mueller2016thesis,
Title = {Multi-rotor aircraft collision avoidance using partially observable {M}arkov decision processes},
Author = {Mueller, Eric R.},
School = {Stanford University},
Year = {2016},
Url = {http://purl.stanford.edu/rv444dz2833}
}

Urban Air Mobility Uncorrelated Model

Model for Urban Air Mobility (UAM) trajectories at low altitudes (mostly takeoff and landing trajectories). Trajectories are generated by sampling trajectory features and using them to constain a convex optimization problem that selects the position at each time step.

Terminal Encounter Model

The MIT LL Terminal Encounter Model (LLTEM) was developed to generate statistically representative encounters between unmanned aircraft and manned aircraft in terminal airspace. The model presently addresses unmanned aircraft on straight-in approach to a Class D airport encountering a second aircraft either landing or taking off. This is the one of the few models that explicitly models two aircraft.

Note a technical description of this model has not been publicly released yet. This model was developed to support UAS well clear research.

Encounter Categories

The previous Documentation section provided technical details for each of the models. This section describes the different encounter categories when pairing trajectories from the models. Generally speaking, encounters can be correlated (e.g. ATC involvement) or uncorrelated (e.g. aircraft blunder into close proximity).

This section describes the encounter categories for different pairing of aircraft. The first column denotes the aircraft of interest (ownship) while the first row denotes the intruder aircraft. These are the primary encounter categories:

• C = correlated, assumes ATC involvement
• D = uncorrelated, due regard oceanic state aircraft without ATC involvement
• U = uncorrelated aircraft likely equipped with transponders but without ATC involvement
• V = uncorrelated unconventional aircraft likely without transponders and without ATC involvement
• X = not required or applicable
• ? = unknown, in development or has not been defined

Manned vs Manned

Unmanned vs Manned

Unmanned vs Unmanned

The aviation community is currently focused on enabling UAS airspace integration and unmanned vs manned encounters. The SARP is currently working towards defining encounter categories for unmanned vs unmanned encounters. This section is scheduled to be updated in 2020.

Download

To provide the community with benchmarks and reduce the barrier of entry to use the encounter models, specific trajectory and encounter sets will be sampled and released to the public. As these become available, the tables below will be updated. The statistical encounter models are currently available through correspondence with the administrators.

Terminology

For an uncorrelated encounter, a single aircraft trajectory is sampled from an encounter model which is paired with a different, separately sampled trajectory to create one encounter. A collection of encounters, often used for Monte Carlo simulations, are referred to as an encounter set. For correlated encounters, both aircraft are sampled together from the same encounter model.

Trajectories

Encounter Sets

Select Applications and Use Cases

A historical perspective and example applications of the encounter models for the TCAS and UAS well clear are presented here.

Clicking the arrow next the citation will display the Bibtex entry for each citation. Each Bibtex entry includes a URL to access the citation. All citations can also be found in the Zotero group, airspace-encounter-model.

Historical Perspective

Airspace encounter models have evolved significantly over the past 35 years. Starting in the 1980s with a model of aircraft equipped with transponders that are cooperatively sharing information and allowed only two-dimensional (vertical plane motion). It was built by the MITRE Corporation from 12 radar sites and supported the development and certification of the TCAS. The 1990s saw the International Civil Aviation Organization (ICAO) and Eurocontrol and lead development of simplified models with three-dimensional motion. Notably, the lowest altitude considered by the Eurocontrol model version 2.1 was 1,000-5,000 ft. AGL. This was indicative of the encounter models support for large manned aircraft avoidance.

The next major encounter model advancement started in 2006, with recognizing the need for three-dimension models with multiple acceleration points to support manned and unmanned safety analysis. In response starting in 2008, MIT Lincoln Laboratory started developing more advanced encounter models to represent a wider range of encounters.

The following are recommended seminal citations for the original encounter model development in the 1980s up to the early 2000s, immediately prior to the development of the MIT Lincoln Laboratory encounters models. Please refer to the Documentation section for details on the MIT Lincoln Laboratory encounter models and development since 2008.

@techreport{zeitlinCollisionAvoidanceUnmanned2006,
	title = {Collision {Avoidance} for {Unmanned} {Aircraft}: {Proving} the {Safety} {Case}},
	shorttitle = {Collision {Avoidance} for {Unmanned} {Aircraft}},
	url = {https://apps.dtic.mil/docs/citations/ADA474336},
	language = {en},
	number = {MP-060219},
	urldate = {2019-01-16},
	institution = {The MITRE Corporation and Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Zeitlin, Andrew and Lacher, Andrew and Kuchar, James and Drumm, Ann},
	month = oct,
	year = {2006},
	pages = {12}
}

@techreport{miquelEuropeanEncounterModel2001,
	type = {Technical {Report}},
	title = {European {Encounter} {Model}: specifications and probability tables},
	number = {ACASA/WP1/186 version 2.1},
	institution = {CENA/Sofréavia and QinetiQ},
	author = {Miquel, Thierry and Thierry, Kevin},
	month = dec,
	year = {2001}
}

@techreport{mclaughlinSafetyStudyTraffic1997,
	type = {{MITRE} {Technical} {Report}},
	title = {Safety study of the {Traffic} {Alert} and {Collision} {Avoidance} {System} ({TCAS} {II})},
	number = {MTR 97W32},
	institution = {MITRE Corporation},
	author = {McLaughlin, Michael P.},
	month = jun,
	year = {1997}
}

@techreport{lebronSystemSafetyStudy1983,
	type = {Final {Report}},
	title = {System {Safety} {Study} of {Minimum} {TCAS} {II}},
	url = {https://apps.dtic.mil/docs/citations/ADA138674},
	number = {MTR-83W241},
	institution = {The MITRE Corporation},
	author = {Lebron, John},
	month = dec,
	year = {1983},
	pages = {376}
}

Well Clear for UAS

The UAS ExCOM SARP supported the inital development of the discriminative uncorrelated UAS model to support sUAS research, while also primarily leveraging the predating uncorrelated manned model. The SARP paired and simulated the UAS with manned aircraft trajectories, sampled from the manned models, to evaluate various candidates and subsequently recommend quantitative means to remain well clear for sUAS. These recommendations have been transitioned to standards developing organizations for when considering tactical DAA performance requirements.

@inproceedings{lesterThreeQuantitativeMeans2019,
	address = {Herndon, VA, USA},
	title = {Three {Quantitative} {Means} to {Remain} {Well} {Clear} for {Small} {UAS} in the {Terminal} {Area}},
	url = {https://doi.org/10.1109/ICNSURV.2019.8735171},
	doi = {10.1109/ICNSURV.2019.8735171},
	booktitle = {2019 {Integrated} {Communications}, {Navigation} and {Surveillance} {Conference} ({ICNS})},
	author = {Lester, Edward Ted and Weinert, Andrew},
	month = apr,
	year = {2019},
	pages = {1--17}
}

@article{weinertWellClearRecommendationSmall2018,
	title = {Well-{Clear} {Recommendation} for {Small} {Unmanned} {Aircraft} {Systems} {Based} on {Unmitigated} {Collision} {Risk}},
	volume = {26},
	url = {https://doi.org/10.2514/1.D0091},
	doi = {10.2514/1.D0091},
	number = {3},
	urldate = {2019-01-09},
	journal = {Journal of Air Transportation},
	author = {Weinert, Andrew and Campbell, Scot and Vela, Adan and Schuldt, Dieter and Kurucar, Joel},
	year = {2018},
	pages = {113--122}
}

@inproceedings{cookDefiningWellClear2015,
	address = {Kissimmee, Florida},
	series = {{AIAA} {SciTech} {Forum}},
	title = {Defining {Well} {Clear} for {Unmanned} {Aircraft} {Systems}},
	url = {https://arc.aiaa.org/doi/10.2514/6.2015-0481},
	doi = {10.2514/6.2015-0481},
	urldate = {2019-01-09},
	booktitle = {{AIAA} {Infotech} @ {Aerospace}},
	publisher = {American Institute of Aeronautics and Astronautics},
	author = {Cook, Stephen P. and Brooks, Dallas and Cole, Rodney and Hackenberg, Davis and Raska, Vincent},
	month = jan,
	year = {2015}
}

@inproceedings{chenDefiningWellClear2019,
	address = {Dallas, Texas},
	title = {Defining {Well} {Clear} {Separation} for {Unmanned} {Aircraft} {Systems} {Operating} with {Noncooperative} {Aircraft}},
	url = {https://arc.aiaa.org/doi/abs/10.2514/6.2019-3512},
	doi = {10.2514/6.2019-3512},
	language = {en},
	urldate = {2019-08-20},
	booktitle = {{AIAA} {Aviation} 2019 {Forum}},
	publisher = {American Institute of Aeronautics and Astronautics},
	author = {Chen, Christine and Edwards, Matthew W. and Gill, Bilal and Smearcheck, Samantha and Adami, Tony and Calhoun, Sean and Wu, Minghong G. and Cone, Andrew and Lee, Seung Man},
	month = jun,
	year = {2019}

@inproceedings{weibelEstablishingRiskBasedSeparation2011,
	title = {Establishing a {Risk}-{Based} {Separation} {Standard} for {Unmanned} {Aircraft} {Self} {Separation}},
	doi = {10.2514/6.2011-6921},
	booktitle = {Ninth {USA}/{Europe} {Air} {Traffic} {Management} {Research} \& {Development} {Seminar}},
	publisher = {American Institute of Aeronautics and Astronautics},
	author = {Weibel, Roland E. and Edwards, Matthew W. M. and Fernandes, Caroline S.},
	url = {https://doi.org/10.2514/6.2011-6921},
	month = sep,
	year = {2011}
}

Evaluation of TCAS II Version 7.1

The modern encounter models were first used for a safety study, funded by the TCAS program office. These generative models evaluated and validated the upgrade of TCAS II to Version 7.1. The success of this evaluation laid the groundwork for subsequent safety studies.

@techreport{espindleSafetyAnalysisUpgrading2009,
	type = {Project {Report}},
	title = {Safety {Analysis} of {Upgrading} to {TCAS} {Version} 7.1 {Using} the 2008 {U}.{S}. {Correlated} {Encounter} {Model}},
	number = {ATC-349},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Espindle, L. P. and Griffith, J. D. and Kuchar, J. K.},
	url = {https://www.ll.mit.edu/r-d/publications/safety-analysis-upgrading-tcas-version-71-using-2008-us-correlated-encounter-model},
	year = {2009}
}

@techreport{chludzinskiEvaluationTCASII2009,
	type = {Project {Report}},
	title = {Evaluation of {TCAS} {II} {Version} 7.1 {Using} the {FAA} {Fast}-{Time} {Encounter} {Generator} {Model}},
	number = {ATC-346 Volume 1},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Chludzinski, Barbara J.},
	url = {https://www.ll.mit.edu/r-d/publications/evaluation-tcas-ii-version-71-using-faa-fast-time-encounter-generator-model-volume},
	month = apr,
	year = {2009}
}

@techreport{chludzinskiEvaluationTCASII2009a,
	type = {Project {Report}},
	title = {Evaluation of {TCAS} {II} {Version} 7.1 {Using} the {FAA} {Fast}-{Time} {Encounter} {Generator} {Model}-{Appendix}},
	number = {ATC-346 Volume 2},
	institution = {Massachusetts Institute of Technology, Lincoln Laboratory},
	author = {Chludzinski, Barbara J.},
	url = {https://www.ll.mit.edu/r-d/publications/evaluation-tcas-ii-version-71-using-faa-fast-time-encounter-generator-model},
	month = apr,
	year = {2009}
}

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