Tactical Conflict Detection in Terminal Airspace

Tang, Huabin; Robinson, John E.; Denery, Dallas G.

doi:10.2514/1.51898

Cited by 31 publications

(54 citation statements)

References 7 publications

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“…Flight intent information currently used by T-TSAFE includes Nominal Interior Routes (NIRs), Area Navigation (RNAV) Departure Procedures (DPs), flight plan information, and altitude clearances [2].…”

Section: A Flight Intent Informationmentioning

confidence: 99%

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Tactical Conflict Detection with Altitude Restrictions in Terminal Airspace

Tang

2019

Journal of Air Transportation

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Automated conflict detection in terminal airspace is a challenging problem, in part because key information regarding flight intent is not available. Unlike en route controllers, terminal controllers currently do not enter altitude clearances when they issue them by voice, so the altitude at which a flight will level off is unavailable to the conflict detection automation. This uncertainty increases the likelihood of false alerts. Requiring terminal controllers to make entry of altitude clearances may be met with resistance due to workload concerns. A new method is studied in which a ground-based trajectory predictor predicts altitude leveloffs a priori, based on altitude restrictions on published runway descent profiles and at waypoints on Nominal Interior Routes (NIRs) and Area Navigation (RNAV) Departure Procedures (DPs). The full benefit of the intent information of all altitude clearances is shown to be achievable when controllers only make entry of altitude clearances not attributable to altitude restrictions, which leads to an insignificant workload increase; an unacceptable level of workload increase may result if all altitude clearances are entered during busy hours. In addition, the integration of altitude restriction increases the average alert lead time to 43 seconds from 37 seconds without. 2 The inherent complexities of terminal operations pose a number of challenges in the development of automated tools that alert air traffic controllers to potential conflicts. Routine large-angle turns without well-defined intent information before final approaches requires proper handling to prevent a proliferation of nuisance alerts: alerts that do not provide useful information beyond what the controllers know and are not necessary to maintain safety [1]. Spacing of aircraft near standard separation thresholds to maximize arrival and departure throughput increases the difficulty of predicting separation conflicts without causing false alerts: alerts on predicted losses of separation that do not materialize, with no indication of any controller or pilot intervention [2]. The difficulty also stems from the dynamic and complex nature of the standard separation criteria, which depend on relative course, aircraft weight classes, distance to the runway, and other factors [3]. The Common Automated Radar Terminal System (CARTS) [4], which has been operational since the 1980s, and the recent Standard Terminal Automation Replacement System (STARS) [5] have a Conflict Alert (CA) functionalitythat generates safety alerts when CA determines that two aircraft are in dangerous proximity to one another or are on a course that will put them in dangerous proximity to one another. The Federal Aviation Administration (FAA) analyzed CA performance and classified 80% of CA alerts in terminal airspace as nuisance [1,6]. The analysis suggested flight-intent information to be incorporated in conflict detection algorithms so the nuisance alerts may be reduced.A ground-based initial prototype system that incorporates flight intent inform...

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Section: A Flight Intent Informationmentioning

confidence: 99%

“…STARS does have the necessary information described above for selecting NIRs and RNAV DPs. Departure runways were assumed to be given previously [2]; they are now detected automatically. Altitude clearances that are not attributable to altitude restrictions are expected to be entered into the system by controllers.…”

Section: A Flight Intent Informationmentioning

confidence: 99%

Tactical Conflict Detection with Altitude Restrictions in Terminal Airspace

Tang

2019

Journal of Air Transportation

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“…NASA has developed a tactical conflict detection and resolution tool specifically designed for the complexities of the terminal airspace that is based on TSAFE, called the Terminal Tactical Separation Assured Flight Environment, or Terminal TSAFE (T-TSAFE). 4 An important safety goal we focus on in this work is to maintain safe separation between all aircraft. Maintaining separation in the terminal airspace (near an airport) is much more challenging than maintaining separation in en route airspace.…”

Section: Introductionmentioning

confidence: 99%

“…By combining all of these variables, T-TSAFE is able to predict the future positions of aircraft and check them for possible conflicts with significantly fewer false alerts than the current legacy system. 4 Obviously, it is important to ensure T-TSAFE is working correctly and produces as few prediction mistakes as possible, even under off-nominal conditions or when the normal behavior of the air traffic system changes. Our goal is to work towards the validation of T-TSAFE within this safety-critical system.…”

Section: Introductionmentioning

confidence: 99%

Validating an Air Traffic Management Concept of Operation using Statistical Modeling

Davies

2013

AIAA Modeling and Simulation Technologies (MST) Conference

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Validating a concept of operation for a complex, safety-critical system (like the National Airspace System) is challenging because of the high dimensionality of the controllable parameters and the infinite number of states of the system. In this paper, we use statistical modeling techniques to explore the behavior of a conflict detection and resolution algorithm designed for the terminal airspace. These techniques predict the robustness of the system simulation to both nominal and off-nominal behaviors within the overall airspace. They also can be used to evaluate the output of the simulation against recorded airspace data. Additionally, the techniques carry with them a mathematical value of the worth of each prediction-a statistical uncertainty for any robustness estimate. Uncertainty Quantification (UQ) is the process of quantitative characterization and ultimately a reduction of uncertainties in complex systems. UQ is important for understanding the influence of uncertainties on the behavior of a system and therefore is valuable for design, analysis, and verification and validation. In this paper, we apply advanced statistical modeling methodologies and techniques on an advanced air traffic management system, namely the Terminal Tactical Separation Assured Flight Environment (T-TSAFE). We show initial results for a parameter analysis and safety boundary (envelope) detection in the high-dimensional parameter space. For our boundary analysis, we developed a new sequential approach based upon the design of computer experiments, allowing us to incorporate knowledge from domain experts into our modeling and to determine the most likely boundary shapes and its parameters. We carried out the analysis on system parameters and describe an initial approach that will allow us to include time-series inputs, such as the radar track data, into the analysis.

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“…Current Next Generation Air Transportation System (NextGen) Terminal Area research efforts aim to improve accuracy and reduce false alerts of legacy terminal area decision support tools by applying new trajectory prediction methods. 5,6 One such terminal area decision support tool is the FAA's Terminal Proximity Alert (TPA) system.…”

Section: Introductionmentioning

confidence: 99%

A Final Approach Trajectory Model for Current Operations

Gong

Sadovsky

2010

10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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Predicting accurate trajectories with limited intent information is a challenge faced by air traffic management decision support tools in operation today. One such tool is the FAA's Terminal Proximity Alert system which is intended to assist controllers in maintaining safe separation of arrival aircraft during final approach. In an effort to improve the performance of such tools, two final approach trajectory models are proposed; one based on polynomial interpolation, the other on the Fourier transform. These models were tested against actual traffic data and used to study effects of the key final approach trajectory modeling parameters of wind, aircraft type, and weight class, on trajectory prediction accuracy. Using only the limited intent data available to today's ATM system, both the polynomial interpolation and Fourier transform models showed improved trajectory prediction accuracy over a baseline dead reckoning model. Analysis of actual arrival traffic showed that this improved trajectory prediction accuracy leads to improved inter-arrival separation prediction accuracy for longer look ahead times. The difference in mean inter-arrival separation prediction error between the Fourier transform and dead reckoning models was 0.2 nmi for a look ahead time of 120 sec, a 33 percent improvement, with a corresponding 32 percent improvement in standard deviation.

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Tactical Conflict Detection in Terminal Airspace

Cited by 31 publications

References 7 publications

Tactical Conflict Detection with Altitude Restrictions in Terminal Airspace

Tactical Conflict Detection with Altitude Restrictions in Terminal Airspace

Validating an Air Traffic Management Concept of Operation using Statistical Modeling

A Final Approach Trajectory Model for Current Operations

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