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Machine Learning Approach for Aircraft Performance Model Parameter Estimation for Trajectory Prediction ApplicationsThe accurate prediction of aircraft trajectory by ground-based decision support tools (DST) is a major concern in air traffic management (ATM). Aircraft trajectory prediction tools rely on a simplified point-mass aircraft performance model (APM) to make their predictions. Even though the performance coefficients and weight of an aircraft are a vital part of the APM’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to DSTs. The current ATM research focuses on improving the estimate of some APM parameters by freezing all other coefficients. This simplified approach introduces unwanted sources of bias and negatively impacts the accuracy of the performance model. In this paper, we apply machine learning (ML) models for the prediction of three key APM parameters simultaneously (two drag coefficients and the initial aircraft weight). To accomplish this, we employ an ordinary differential equation (ODE) fitting approach to generate optimized APM parameter labels customized to each individual flight record. Subsequently, we train ML models to capture the relationship between the historical data and the optimized APM parameters. Two different ML model solutions are applied and the APM coefficients are predicted for unseen flights. The results indicate that the ML models are able to capture APM parameters relationships with flight related features with good accuracy.
Document ID
20230009758
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Aida Sharif Rohani
(Universities Space Research Association Columbia, Maryland, United States)
Tejas G Puranik
(Universities Space Research Association Columbia, Maryland, United States)
Krishna M Kalyanam
(Ames Research Center Mountain View, California, United States)
Date Acquired
June 29, 2023
Subject Category
Air Transportation and Safety
Meeting Information
Meeting: Digital Avionics Systems Conference
Location: Barcelona
Country: ES
Start Date: October 1, 2023
End Date: October 5, 2023
Sponsors: American Institute of Aeronautics and Astronautics, Institute of Electrical and Electronics Engineers
Funding Number(s)
CONTRACT_GRANT: NNA16BD14C
PROJECT: 031102
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
drag coefficients
air traffic management
aircraft performance model
machine learning
trajectory prediction
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