Review of Trajectory Accuracy Methodology and Comparison of Error Measurement Metrics

Ryan, Hollis F.; Paglione, Mike; Green, Steve

doi:10.2514/6.2004-4787

Cited by 25 publications

(12 citation statements)

References 1 publication

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“…In [12], the authors describe the differences emerging from time-coincident and spatially coincident metrics, particularly when various spatially-coincident schemes are considered. Methods for decomposing along-track and crosstrack elements are described.…”

Section: Prior Accuracy Measuresmentioning

confidence: 99%

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Development of Key Performance Indicators for Trajectory Prediction Accuracy

Mondoloni¹

2006

2006 Ieee/Aiaa 25TH Digital Avionics Systems Conference

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Key Performance Indicators (KPI) for trajectory prediction accuracy were developed by applying factor analysis to a wider set of accuracy metrics obtained from a literature search. A MonteCarlo simulation was conducted under operationally-representative conditions to provide a data set for the analysis. It is shown that the derived KPI can be linearly combined to estimate the larger set of metrics. These estimates provide good rank correlation with the actual metrics computed. KPIs can describe both the accuracy of trajectory prediction in addition to the quality of the input data supplied to a trajectory predictor. Various applications of these KPI are discussed including the specification of requirements on prediction performance. While certain KPI are described in this study, various values could have been selected. It is argued that TP KPIs should be made consistent with measurements used to express vertical and longitudinal RNP as those get defined.

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Section: Prior Accuracy Measuresmentioning

confidence: 99%

“…Prior investigations in the area of trajectory prediction accuracy have described some necessary considerations with measurement of trajectory prediction accuracy ([3]- [12]). Some of these issues are summarized below:…”

Section: Prediction Accuracymentioning

confidence: 99%

Development of Key Performance Indicators for Trajectory Prediction Accuracy

Mondoloni¹

2006

2006 Ieee/Aiaa 25TH Digital Avionics Systems Conference

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“…Typical metrics are the cross-and along-track deviations that can be measured with respect to the closest flight segment or a time-matched segment [9], or the estimated time of arrival (ETA) deviation at a given waypoint [10].…”

Section: B Trajectory Prediction Accuracymentioning

confidence: 99%

Quantification of aircraft trajectory prediction uncertainty using polynomial chaos expansions

Casado

Civita

Vilaplana

et al. 2017

2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)

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Abstract-A novel approach to quantify the uncertainty associated with any aircraft trajectory prediction based on the application of the Polynomial Chaos (PC) theory is presented. The proposed method relies on univariate polynomial descriptions of the uncertainty sources affecting the trajectory prediction process. Those descriptions are used to build the multivariate polynomial expansions that represent the variability of the aircraft state variables along the predicted trajectory. A case study compares the results obtained by a classical Monte Carlo approach with those generated by applying the so-called arbitrary Polynomial Chaos Expansions (aPCE). The results provided herein lead to conclude that this new methodology can be used to accurately quantify trajectory prediction uncertainty with a very low computational effort, enabling the capability of computing the uncertainty of the individual trajectories of a traffic sample of thousands flights within very short time intervals.

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“…As a result, trajectory prediction (TP) and the treatment of trajectory prediction uncertainty continue as active areas of research and development (e.g. [14][15][16][17][18]). Accuracy of TP is generally defined as point spatial accuracy (goal attainment) or as trajectory following accuracy.…”

Section: Trajectory Predictionmentioning

confidence: 99%

4D Trajectories: A functional data perspective

Puechmorel

Delahaye

2007

2007 IEEE/AIAA 26th Digital Avionics Systems Conference

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Abstract3D or 4D trajectories are fundamental objects within the frame of ATM and can be defined as mappings from a bounded interval to the space 3 R .However, this functional aspect is almost never used in applications. A great improvement over existing procedures for statistical analysis of trajectories can be obtained by explicitly considering trajectory data as functional data. Since raw functional data is by essence infinite dimensional, no computation can be made unless a finite representation has been found: all the complexity of functional data analysis is hidden in this stage. The present paper will first analyze the functional aspect of trajectories in order to extract operationally relevant quantities then will give a way of representing optimally aircraft trajectories with a finite number of parameters, allowing further processing. Applications to trajectory prediction and classification will be briefly discussed at the end.

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Review of Trajectory Accuracy Methodology and Comparison of Error Measurement Metrics

Cited by 25 publications

References 1 publication

Development of Key Performance Indicators for Trajectory Prediction Accuracy

Development of Key Performance Indicators for Trajectory Prediction Accuracy

Quantification of aircraft trajectory prediction uncertainty using polynomial chaos expansions

4D Trajectories: A functional data perspective

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