TRACON Aircraft Arrival Planning and Optimization through Spatial Constraint Satisfaction

Bergh, C.; Krzeczowski, K.; Davis, Thomas J.

doi:10.2514/atcq.3.2.117

Cited by 8 publications

(4 citation statements)

References 0 publications

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“…Moreover, the arrival plan cannot depart too drastically from the standard techniques used by the controller. 11…”

Section: Controllability Of the Sequence Advisoriesmentioning

confidence: 99%

“…This section describes the knowledge-based sequencing (KBS) algorithm which combines the trajectory based sequencing of the spatial constraint satisfaction method with fuzzy reasoning to produce acceptable sequence advisories for tactical control in the terminal area. 11 Inputs to the Sequencing Algorithm…”

Section: Passive Fast Sequencing Algorithmmentioning

confidence: 99%

“…Based on the above, an initial spatial constraint satisfaction algorithm was designed to schedule aircraft at multiple points along their trajectories, not just at the runway threshold or meter fix. 11 Like the TMA scheduling algorithm, this early FAST scheduling algorithm utilized accurate 4D trajectories as inputs. All merges within the extended terminal area were considered by breaking the large global sequencing problem of merging at the runway threshold into a network of smaller local sequencing problems of merges at intersections of trajectory segments.…”

Section: Introductionmentioning

confidence: 99%

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A fuzzy reasoning-based sequencing of arrival aircraft in the terminal area

Robinson

Davis

Isaacson

1997

Guidance, Navigation, and Control Conference

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A fuzzy reasoning-based method for scheduling air traffic in the terminal area has been designed at the NASA Ames Research Center and operationally tested at the Dallas/Fort Worth Terminal Radar Approach Control (TRACON). The scheduling system sequences and assigns landing times to arrival aircraft by utilizing continuous updates of aircraft radar data and controller inputs. The scheduling algorithm contains a knowledge base which was refined during several thousand hours of controller-in-the-loop real-time simulations. This knowledge base applies fuzzy reasoning to evaluate propositions that consider both performance criteria and workload criteria, such as delay reduction and conflict avoidance. Operational test results show that the algorithm determines an efficient arrival plan to land aircraft in a manner acceptable to the air traffic controllers. This paper details the scheduling algorithm and presents data regarding its effectiveness in predicting the landing order of arrival aircraft.

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“…Moreover, the arrival plan cannot depart too drastically from the standard techniques used by the controller. 11…”

Section: Controllability Of the Sequence Advisoriesmentioning

confidence: 99%

Section: Passive Fast Sequencing Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A fuzzy reasoning-based sequencing of arrival aircraft in the terminal area

Robinson

Davis

Isaacson

1997

Guidance, Navigation, and Control Conference

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“…Passive FAST is a decision aid to the air traffic controller which consists of two key modules. [1][2][3][4][5][6][7][8][9][10] The first is the Trajectory Synthesizer (TS) which is used to predict the aircraft trajectory history to the runway threshold based on the current time, position, and speed. 9,10 The second module is the Scheduling Logic (SL) which determines the air traffic controller preferred sequencing of aircraft within a flight segment or merging of aircraft onto a common flight segment.…”

Section: Introductionmentioning

confidence: 99%

Final Approach Spacing Tool (FAST) velocity accuracy performance analysis

Mueller¹,

Robinson

1998

Guidance, Navigation, and Control Conference and Exhibit

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The NASA passive Final Approach Spacing Tool (FAST) provides advisory information to the terminal air traffic controller for sequencing and merging of landing aircraft. It consists of the Trajectory Synthesizer (TS) and the Scheduling Logic (SL). FAST has undergone extensive field testing and performance evaluations of the FAST TS algorithms have been conducted. However, no analytical performance evaluations of the FAST SL logic have been performed to date. This paper evaluates the sensitivity of the FAST SL algorithms to velocity errors which are introduced by the radar tracking software. A linear error covariance analysis was performed of the terminal area radar and its alpha-beta tracking filter software. For comparison, an alternate Kalman tracking filter was also evaluated. Using these radar tracking velocity estimation error histories for a typical aircraft flight profile, the FAST TS estimated time of arrival (ETA) statistics history was obtained. To determine the impact of the velocity and ETA errors on FAST SL, figures of merit were defined which predict the probability that FAST SL will reach an incorrect aircraft sequencing decision. These figures of merit were then evaluated, based on the velocity and ETA statistics histories.

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