Analysis of the Aircraft to Aircraft Conflict Properties in the National Airspace System

Paglione, Mike; Santiago, Confesor; Crowell, Andrew; Hughes, W. J.; Oaks, Robert D.

doi:10.2514/6.2008-7143

Cited by 11 publications

(14 citation statements)

References 4 publications

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“…It is not clear, however, whether the poor performance of the parametric model reported in that study is due to issues with the speed look-up tables or if it is really a problem with the algorithm. Reference [9] reports a horizontal error (distance from radar reported position and time-coincident trajectory predicted position) for ERAM that grows near linearly from 0.8 nmi to 6 nmi for look-ahead times of 0 to 20 min. Using an extensive database of recorded field data in the USA, Europe and Australia researchers show that lateral intent errors (in the USA sample mostly) are the dominant source of trajectory errors [10].…”

Section: Trajectory Accuracymentioning

confidence: 98%

Determination and ranking of trajectory accuracy factors

Torres

2010

29th Digital Avionics Systems Conference

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Trajectory accuracy improvements provide the opportunity to reduce fuel consumption and emissions by increasing the predictability of the National Airspace System (NAS). In addition to the environmental benefits, the ability to improve trajectory prediction accuracy enables trajectory based operations (TBO). Because of the foundational reliance on accurate gate-to-gate four dimensional trajectory (4DT) predictions in TBO, trajectory predictors (TP) will have to meet stringent accuracy performance requirements. There have been significant advances in understanding the accuracy performance and limitations of different TP algorithms. However, implementation of TBO requires identification of the specific aspects of trajectory prediction that will need improvement in order to deliver the necessary accuracy. The challenge is not to select a single trajectory model approach but rather to define which modeling approaches can deliver better performance under different circumstances and to understand the limitations of each. A clear understanding of accuracy factors and the way they affect different modeling approaches allows development of hybrid algorithms that combine the strengths of different approaches. It is known, for instance, that kinetic models are affected by the uncertainty in aircraft mass, while parametric models are affected by the variance in the velocities encoded in lookup tables, but what is the relative contribution of these errors to the overall accuracy of predictions? The paper presents an accuracy factors analysis methodology to rank the sources of error according to their respective impact. This analysis facilitates the identification of the modeling issues that have the largest impact in prediction accuracy and the systematic evaluation of the potential improvements that could be expected from the use of aircraft intent information that will be available when air-ground data link is deployed. The sources of errors in trajectory prediction have been amply studied; however, a clear understanding of the relative contribution of these errors to accuracy performance using realistic scenarios is necessary in order to be able to improve the models. The accuracy factors ranking methodology presented here relies on the computation of the Past Maximum Deviation (PMD) metric for each error measurement and using the PMD to attribute the error to one of four possible error source categories: lateral, vertical, velocity and heading. The PMD metric keeps track of the largest track-trajectory deviation prior to the measurement along each of the four categories. PMD distributions serve as a diagnostic that reveal the performance of a trajectory predictor (TP) along the four dimensions discussed and can be used as an effective tool to compare two TPs or two variants of the same TP. Results of accuracy factor analysis based on the En Route Automation Modernization ERAM parametric algorithm using a live recorded scenario are presented.

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Section: Trajectory Accuracymentioning

confidence: 98%

Determination and ranking of trajectory accuracy factors

Torres

2010

29th Digital Avionics Systems Conference

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“…Adjusting this parameter allowed for control over the proportion of each type of resolution selected by the Autoresolver An analysis of current operations was performed to help calibrate this parameter. This analysis was an extension of the work by Paglione et al 7 which presented an algorithm to determine the number and geometric properties of conflicts in the current system. For this analysis, their algorithm was extended to determine the type of maneuvers used to resolve each conflict.…”

Section: A Configuring the Autoresolvermentioning

confidence: 98%

A Computer Simulation of the System-Wide Effects of Parallel-Offset Route Maneuvers

Lauderdale

Santiago

Pankok

2010

AIAA Guidance, Navigation, and Control Conference

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Most aircraft managed by air-traffic controllers in the National Airspace System are capable of flying parallel-offset routes. This paper presents the results of two related studies on the effects of increased use of offset routes as a conflict resolution maneuver. The first study analyzes offset routes in the context of all standard resolution types which air-traffic controllers currently use. This study shows that by utilizing parallel-offset route maneuvers, significant system-wide savings in delay due to conflict resolution of up to 30% are possible. It also shows that most offset resolutions replace horizontal-vectoring resolutions. The second study builds on the results of the first and directly compares offset resolutions and standard horizontal-vectoring maneuvers to determine that in-trail conflicts are often more efficiently resolved by offset maneuvers.

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“…Aircraft's cross-track distance from the route has long been used, as a state variable, to compute the likelihood, but other types of variables such as the course deviation, i.e., the angular difference between the current velocity vector of aircraft and the direction of a route segment, have also started being used. [11][12][13][15][16][17] By including an additional state variable, we may improve the performances of conformance monitoring, especially in terms of the early detection of a non-conforming aircraft. However, the prerequisite for including additional variables is to develop reliable statistical models on how those variables are supposed to behave for a given flight plan, otherwise the computed likelihoods for each hypothetical flight plan are simply undependable for decision making.…”

Section: Ib Previous Approaches For Conformance Monitoringmentioning

confidence: 99%

A Bayesian Approach for Conformance Monitoring

Lee

Fukuda

2011

11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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For safe and efficient management of air traffic, the prediction of aircraft trajectories into the future is necessary. Conformance monitoring is one of important functions in trajectory prediction to ensure that aircraft adhere to the assigned flight plan. One of challenges for timely detection of a non-conforming aircraft is that its trajectories cannot be fully specified due to uncertainty on how the aircraft executes the currently applied flight plan. This uncertainty, together with noisy measurements, can create a series of data poor situations which often results in a high level of false detection or detection delay. To overcome this problem, our paper proposes a new method of conformance monitoring by using a Bayesian formulation. A prior available information can be utilized in the proposed method to reduce false detections without an arbitrary increase in detection delay. Furthermore, the proposed method can provide actual probability of conformance which can bring various capabilities to trajectory prediction. The proposed method is illustrated with examples.Nomenclature z(k) measurement of aircraft's state at time k Z(k) a set of all measurements up to time k T CT trajectory change time for a non-conforming aircraft Ω a set two conformance statuses; ω 0 -conformance and ω 1 -non-conformance H a set of TCT hypotheses

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Analysis of the Aircraft to Aircraft Conflict Properties in the National Airspace System

Cited by 11 publications

References 4 publications

Determination and ranking of trajectory accuracy factors

Determination and ranking of trajectory accuracy factors

A Computer Simulation of the System-Wide Effects of Parallel-Offset Route Maneuvers

A Bayesian Approach for Conformance Monitoring

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