Well Clear Trade Study for Unmanned Aircraft System Detect And Avoid with Non-Cooperative Aircraft

Wu, Minghong G.; Cone, Andrew C.; Lee, Seungman; Chen, Christine; Edwards, Matthew; Jack, Devin P.

doi:10.2514/6.2018-2876

Cited by 18 publications

(16 citation statements)

References 17 publications

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“…As with the first phase, human factors considerations will be essential in defining the requirements associated with these new applications. Early simulation work has already helped to identify necessary modifications to the well-clear thresholds for alerting and guidance in the context of second phase (see Fern et al, 2018;Vincent et al, 2018;Wu et al, 2018). As noted earlier, much of this research is being conducted by organizations participating in Special Committee 228.…”

Section: Implications and Future Directionsmentioning

confidence: 99%

Human Factors Contributions to the Development of Standards for Displays of Unmanned Aircraft Systems in Support of Detect-and-Avoid

Rorie

Fern

et al. 2020

Hum Factors

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Objective The aim is to provide a high-level synthesis of human factors research that contributed to the development of detect-and-avoid display requirements for unmanned aircraft systems (UAS). Background The integration of UAS into the U.S. National Airspace System is a priority under the Federal Aviation Administration’s Modernization and Reform Act. For UAS to have routine access to the National Airspace System, UAS must have detect-and-avoid capabilities. One human factors challenge is to determine how to display information effectively to remote pilots for performing detect-and-avoid tasks. Method A high-level review of research informing the display requirements for UAS detect-and-avoid is provided. In addition, description of the contributions of human factors researchers in the writing of the requirements is highlighted. Results Findings from human-in-the-loop simulations are used to illustrate how evidence-based guidelines and requirements were established for the display of information to assist pilots in performing detect-and-avoid. Implications for human factors are discussed. Conclusion Human factors researchers and engineers made many contributions to generate the data used to justify the detect-and-avoid display requirements. Human factors researchers must continue to be involved in the development of standards to ensure that requirements are evidence-based and take into account human operator performance and human factors principles and guidelines. Application The research presented in this paper is relevant to the design of UAS, the writing of standards and requirements, and the work in human–systems integration.

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Section: Implications and Future Directionsmentioning

confidence: 99%

Human Factors Contributions to the Development of Standards for Displays of Unmanned Aircraft Systems in Support of Detect-and-Avoid

Rorie

Fern

et al. 2020

Hum Factors

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“…In this section, Low SWaP DWC refers collectively to four DWC definitions being evaluated for lighter UAS operating below 10,000 ft with typical speeds below 100 knots. These definitions were selected from a larger pool of candidates investigated in [17]. Phase 1 DWC refers to the definition adopted for Phase 1 UAS MOPS and which, with its larger thresholds, is a superset of all the Low SWaP DWC definitions.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…The MIT encounter model was used to provide mitigated and unmitigated probabilities and distributions of various events such as TCAS RA and well clear violations. In [17] the NASA encounter model was used to evaluate several candidate DWC definitions for low cost, size, weight, and power UAS. The MIT encounter model was used to complement and validate results of the NASA encounter model.…”

mentioning

confidence: 99%

Encounter-Based Simulation Architecture for Detect-And-Avoid Modeling

Refai¹,

Abramson²,

Lee³

et al. 2019

AIAA Scitech 2019 Forum

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This paper presents an encounter-based simulation architecture developed to facilitate flexible and efficient detect and avoid modeling in parametric or trade-space studies on large data sets. The basic premise of this tool is that large-scale input data can be reduced to a set of "canonical encounters" and that using the reduced data in simulations does not lead to loss of fidelity. A canonical encounter is specified as ownship and intruder flight portions potentially resulting in a loss of well clear along with a set of properties that characterize the encounter. The advantages of using canonical encounters include faster simulations, reduced memory footprint, ability to select encounters based on user-specified criteria, shared encounters across multiple teams, peer-reviewed encounters, and a better understanding of the input data set, to name a few. The performance of the encounter-based approach is compared to the approach used previously, which modeled flights from departure to destination using the Java Architecture for DAA Extensibility and Modeling (JADEM). The new architecture reduced the amount of data to be processed a hundredfold and the total computation time five-fold.

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“…As an alternative method of analysis, contour plots were produced to analyze the effects of various DAA Well Clear definitions for aircraft that desire to carry low cost, size, weight, and power sensors [4]. A sample contour plot is shown in Fig.…”

Section: Contour Plotmentioning

confidence: 99%

“…This work's companion paper [3] considers and presents the benefits to adjusting these performance requirements to benefit UAS with higher turning capabilities or providing reduced sensor or alerting requirements to accommodate UAS that can operate in more beneficial speed bands. In contrast, this paper explores the effects of changing DAA Well Clear (DWC) definition thresholds on minimum UA maneuver initiation range and time to LoWC, and describes an alternative method of analysis using contour plots to facilitate current RTCA SC-228 work to adjust the established DWC definition based on the operating airspace or UA performance [4].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Detect and Avoid Well Clear Parameter Variations on UAS DAA Sensor Requirements

Hardy¹,

Jack²,

Hoffler³

2018

2018 Aviation Technology, Integration, and Operations Conference

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In support of NASA's Unmanned Aircraft Systems Integration in the National Airspace System project and RTCA Special Committee 228, an analysis has been performed to provide insight in to the trade space between detect and avoid (DAA) Well Clear definition threshold variations, which could affect DAA sensor range and alerting requirements. I. NomenclatureCPA = closest point of approach deg = degrees = range in the x-dimension = range in the y-dimension dh = vertical separation DMOD = distance modifier ft = feet h = vertical separation threshold HMD = horizontal miss distance KCAS = knots calibrated airspeed KTAS = knots true airspeed r = horizontal range ̇ = horizontal closure rate sec = second = time to closest point of approach (CPA) ℎ = time to achieve vertical separation = modified tau = relative velocity in the x-dimension = relative velocity in the y-dimension II. IntroductionThe integration of unmanned aircraft systems (UAS) into the U.S.' National Airspace System (NAS) is highly desirable to many stakeholders across government, industry, and academia; maintaining the same level of safety as is currently in the NAS requires a means of replacing the manned aircraft pilot's obligation to see-and-avoid other aircraft [1]. RTCA Special Committee 228 (SC-228), a federal advisory committee consisting of public and private stakeholders, is developing minimum technical requirements for a detect-and-avoid (DAA) system to be used in lieu of a manned aircraft pilot's see-and-avoid capability. A DAA system uses a suite of sensors, trackers, detection algorithms, and display to provide the remotely located pilot with sufficient awareness to avoid a loss of separation with other aircraft. SC-228's recently published minimum operational performance standards (MOPS) for DAA systems are outlined in , while the accompanying MOPS for air-to-air radar are outlined in . To make the problem more manageable, DO-365 and DO-366 were necessarily developed to support a limited set of UAS performance and operations. Ongoing work is intended to enable more types of UAS and support more complete operations.

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Well Clear Trade Study for Unmanned Aircraft System Detect And Avoid with Non-Cooperative Aircraft

Cited by 18 publications

References 17 publications

Human Factors Contributions to the Development of Standards for Displays of Unmanned Aircraft Systems in Support of Detect-and-Avoid

Human Factors Contributions to the Development of Standards for Displays of Unmanned Aircraft Systems in Support of Detect-and-Avoid

Encounter-Based Simulation Architecture for Detect-And-Avoid Modeling

Sensitivity Analysis of Detect and Avoid Well Clear Parameter Variations on UAS DAA Sensor Requirements

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