Assessing Trajectory Prediction Performance &amp;amp;#8211; Metrics Definition

Mondoloni, Stéphane; Swierstra, S. Doaitse; Paglione, Mike

doi:10.1109/dasc.2005.1563347

Cited by 29 publications

(23 citation statements)

References 10 publications

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“…• Variations of TP functions -This has been dealt with previously through the definition of the TP structure referred to in [10]. One must be consistent in the definition of input data, and consistent in defining TP functionality in order to compare TP performance.…”

Section: Resultsmentioning

confidence: 99%

“…One can decompose the TP accuracy metrics applied in prior investigations into basic metrics to which specialization and derivation is applied (e.g., see [10]). The result of such a process reveals a set of basic metrics that is fairly common across investigations, with differences stemming from specialization and derivation.…”

Section: Accuracy Measures Investigatedmentioning

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: Resultsmentioning

confidence: 99%

Section: Accuracy Measures Investigatedmentioning

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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“…This paper is one of a set of papers produced by the AP16 Core Team. The other papers in this set address the proposed structure of a common trajectory predictor [2], the validation metrics to apply [3], a common method for aircraft intent description [4], and an initial comparison of aircraft performance models [5]. This paper presents the AP16 approach to TP validation.…”

Section: Overview Of Improvement Processmentioning

confidence: 97%

“…• Compare predicted and reference data to support the application of the validation methodology defined in [1] and metrics defined in [3].…”

Section: Tp Test Harnessmentioning

confidence: 99%

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A Collaborative Approach to Trajectory Modeling Validation

Paglione¹,

Garcia-Avello²,

Swierstra³

et al.

24th Digital Avionics Systems Conference

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Air service providers view the growth in future air traffic demand exceeding that of capacity, making it increasingly difficult to maintain yet alone improve the current levels of safety and efficiency. Advanced Air Traffic Management (ATM) and flight deck decision support tool (DST) capabilities are seen as the functional enablers of the future ATM concepts needed to increase capacity by two-threefold. Such automation will provide support in flight data, metering, and conflict prediction/resolution functions to name a few. DST capabilities depend directly on the performance of the underlying trajectory predictor(s) (TP) that provide the anticipated future path of the aircraft. The accuracy of the TP is critical to the success of these DST functions.A joint international effort has begun to develop methodologies and resources for the common development, validation, and improvement of trajectory prediction capabilities. Common methods and resources for TP validation will benefit the ATM automation community at large by providing tools, methods, and validation data that no one organization could afford to develop on their own. A generic TP structure has been constructed and decomposed into individual services that readily map to all the US and European TPs studied. This TP structure enables specific TP elements to be isolated and validated using different types of actual "flown" trajectories from simulation and field/flight observations. A common TP validation strategy has been developed for universal application to each element of the TP structure. The TP validation strategy is complemented by a broad database of actual trajectory recordings posted on a website and formatted in the Extensible Markup Language (XML). The methodology presented here provides the process for any developer to utilize this database to validate and improve their TP's performance. This paper outlines the TP validation strategy, describes the various types of validation data provided, XML format, and tools developed.

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Sequential Monte Carlo methods for multi‐aircraft trajectory prediction in air traffic management

Lymperopoulos

Lygeros

2010

Adaptive Control & Signal

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Accurate prediction of aircraft trajectories is an important part of decision support and automated tools in air traffic management. We demonstrate that by combining information from multiple aircraft at different locations and time instants, one can provide improved trajectory prediction (TP) accuracy. To perform multi-aircraft TP, we have at our disposal abundant data. We show how this multi-aircraft sensor fusion problem can be formulated as a high-dimensional state estimation problem. The high dimensionality of the problem and nonlinearities in aircraft dynamics and control prohibit the use of common filtering methods. We demonstrate the inefficiency of several sequential Monte Carlo algorithms on feasibility studies involving multiple aircraft. We then develop a novel particle filtering algorithm to exploit the structure of the problem and solve it in realistic scale situations. In all studies we assume that aircraft fly level (possibly at different altitudes) with known, constant, aircraft-dependent airspeeds and estimate the wind forecast errors based only on ground radar measurements. Current work concentrates on extending the algorithms to non-level flights, the joint estimation of wind forecast errors and the airspeed and mass of the different aircraft and the simultaneous fusion of airborne and ground radar measurements.

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Assessing Trajectory Prediction Performance &#8211; Metrics Definition

Cited by 29 publications

References 10 publications

Development of Key Performance Indicators for Trajectory Prediction Accuracy

Development of Key Performance Indicators for Trajectory Prediction Accuracy

A Collaborative Approach to Trajectory Modeling Validation

Sequential Monte Carlo methods for multi‐aircraft trajectory prediction in air traffic management

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