Defining Well Clear Separation for Unmanned Aircraft Systems Operating with Noncooperative Aircraft

Chen, Christine; Gill, Bilal; Edwards, Matthew; Smearcheck, Samantha; Adami, Tony; Calhoun, Sean; Wu, Minghong G.; Cone, Andrew C.; Lee, Seungman

doi:10.2514/6.2019-3512

Cited by 19 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Majority of encounters had a HMD and VMD greater than 2200 and 450 feet, respectively, at CPA. This combination of HMD and VMD are notable as it a separation metric used by RTCA SC-228 [27][28][29] and similar to separation criteria used by a different DAA standard published by ASTM F38 [30]. As the OpenSky Network-based model had 25% of encounters with a HMD and VMD of this or less, that encounter set was slightly more stressing to the DAA system and had encounters to estimate safety metrics based on that separation.…”

Section: Horizontal and Vertical Miss Distance Distributionsmentioning

confidence: 90%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Horizontal and Vertical Miss Distance Distributionsmentioning

confidence: 90%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In order to apply one of the candidate DWC definitions from Phase 2 to Phase 1 compliant UAS, it must be shown that the reduced DWC will not adversely affect the safety and alerting metrics. The effect of the reduced DWC on safety metrics is covered in another paper [17]. This paper uses simulation results that do not include any pilot actions to maintain DWC, referred to as "unmitigated" simulations, to determine the effects of a smaller DWC on alerting metrics for the Phase 1 UAS operations.…”

Section: Motivationmentioning

confidence: 99%

Detect-and-Avoid Alerting Performance for High-Speed UAS and Non-Cooperative Aircraft

Cone

Lee

2019

AIAA Aviation 2019 Forum

Self Cite

View full text Add to dashboard Cite

This paper investigates the alerting timeline and required surveillance volume for four candidate Detect and Avoid (DAA) Well Clear (DWC) definitions for Unmanned Aircraft Systems (UAS) and non-cooperative visual flight rules (VFR) traffic. The candidates are proposed in previous work that analyzed unmitigated collision risks and maneuver initiation ranges for UAS carrying low cost, size, weight, and power radar and that fly with true airspeeds under 100 kts. This analysis extends the maximum UAS true airspeed from 100 kts to 291 kts. Overall, the results from this paper suggest that the smaller DWC definitions do not adversely affect average alerting times. Among the candidate DWC's examined, DWC2 has the potential of reducing the surveillance range requirement the most while maintaining the same alerting timeline. DWC2 is defined as a cylinder with a horizontal radius of 2,200 feet and a vertical range of 450 feet above and below the UAS.

show abstract

“…The proposed Phase 2 definitions reduced the horizontal miss distance and/or the modified Tau component but retained the 450 ft vertical miss distance. The proposed definitions were: Definition 1: 2,000 ft and 15 s; Definition 2: 2,200 ft and 0 s; Definition 3: 1,500 ft and 15 s; and Definition 4: 2,500 ft and 25 s (Chen et al 2019). The results of these simulations indicated that Definition 2 would preserve the most alerting time under reduced radar surveillance ranges associated with Low SWaP sensors.…”

Section: Introductionmentioning

confidence: 98%

“…The results of these simulations indicated that Definition 2 would preserve the most alerting time under reduced radar surveillance ranges associated with Low SWaP sensors. Monk, Rorie, Keeler, and Sadler (2020) reported the results of a human-in-the-loop simulation that modeled a UAS with a low SWaP radar (detection range = 3.5 nm) in order to compare DAA well clear Definitions 1 and 2 from Chen et al's (2019) study on pilot and DAA system performance. Twelve active-duty UAS pilots flew simulated UAS that were either slow (60 kts) or fast (100 kts) in speed.…”

Section: Introductionmentioning

confidence: 99%

Impact of UAS with Low Size, Weight, and Power Sensors on Air Traffic Controllers’ Performance and Acceptability Ratings

VanLuven

Diep

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

A human-in-the-loop simulation was conducted to evaluate the impact of Unmanned Aircraft Systems (UAS) with low size, weight, and power (SWaP) sensors operating in a busy, low-altitude sector. Use of low SWaP sensors allow for UAS to perform detect-and-avoid (DAA) maneuvers against non-transponding traffic in the sector. Depending upon the detection range of the low SWaP sensor, the UAS pilot may or may not have time to coordinate with air traffic controllers (ATCos) prior to performing the DAA maneuver. ATCo’s sector performance and subjective ratings of acceptability were obtained in four conditions that varied in UAS-ATCo coordination (all or none) prior to the DAA maneuver and workload (higher or lower). For performance, ATCos committed more losses of separation in high than low workload conditions. They also had to make more flight plan changes to manage the UAS when the UAS pilot did not coordinate DAA maneuvers compared to when they did coordinate the maneuvers prior to execution. Although the ATCos found the DAA procedures used by the UAS in the study to be acceptable, most preferred the UAS pilot to coordinate their DAA maneuvers with ATCos prior to executing them.

show abstract

Defining Well Clear Separation for Unmanned Aircraft Systems Operating with Noncooperative Aircraft

Cited by 19 publications

References 7 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

Detect-and-Avoid Alerting Performance for High-Speed UAS and Non-Cooperative Aircraft

Impact of UAS with Low Size, Weight, and Power Sensors on Air Traffic Controllers’ Performance and Acceptability Ratings

Contact Info

Product

Resources

About