Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques

This paper presents significant enhancements to the vertical reconstruction component of EUROCONTROL’s Surveillance Analysis Support System for ATC Centres (SASS-C). We introduce four key improvements: (1) a novel segmentation algorithm for more precise flight phase identification, (2) an improved i...

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Main Authors: Daniel Amigo, David Sánchez Pedroche, Jesús García, José Manuel Molina, Jekaterina Trofimova, Emmanuel Voet, Benoît Van Bogaert
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Aerospace
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Online Access:https://www.mdpi.com/2226-4310/11/11/900
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author Daniel Amigo
David Sánchez Pedroche
Jesús García
José Manuel Molina
Jekaterina Trofimova
Emmanuel Voet
Benoît Van Bogaert
author_facet Daniel Amigo
David Sánchez Pedroche
Jesús García
José Manuel Molina
Jekaterina Trofimova
Emmanuel Voet
Benoît Van Bogaert
author_sort Daniel Amigo
collection DOAJ
description This paper presents significant enhancements to the vertical reconstruction component of EUROCONTROL’s Surveillance Analysis Support System for ATC Centres (SASS-C). We introduce four key improvements: (1) a novel segmentation algorithm for more precise flight phase identification, (2) an improved invalid height detection process using LOWESS and sliding window analysis, (3) a protection mechanism against simultaneous measurements at the Kalman filter level, and (4) an optimized approach for smooth overshoot correction during segment transitions. These advancements address limitations in the current system, particularly in trajectory segmentation accuracy and robustness against measurement anomalies. Our methodology employs both synthetic and real-world data for comprehensive evaluation, ensuring performance under controlled and operational conditions. The results demonstrate substantial improvements in segmentation precision, outlier detection, and overall trajectory reconstruction quality. The invalid detection algorithm, while incurring a slight computational cost, significantly enhances trajectory accuracy. These enhancements contribute to more reliable air traffic analysis, supporting safer and more efficient airspace management. The paper concludes by discussing potential future work, including the application of machine learning techniques and the extension of these improvements to horizontal reconstruction processes.
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spelling doaj-art-d04532774a8f460fbb2c5d44c76072d32025-08-20T02:26:50ZengMDPI AGAerospace2226-43102024-10-01111190010.3390/aerospace11110900Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering TechniquesDaniel Amigo0David Sánchez Pedroche1Jesús García2José Manuel Molina3Jekaterina Trofimova4Emmanuel Voet5Benoît Van Bogaert6Group GIAA, University Carlos III of Madrid, Avd. de Gregorio Peces-Barba Martínez, 22, 28270 Colmenarejo, SpainGroup GIAA, University Carlos III of Madrid, Avd. de Gregorio Peces-Barba Martínez, 22, 28270 Colmenarejo, SpainGroup GIAA, University Carlos III of Madrid, Avd. de Gregorio Peces-Barba Martínez, 22, 28270 Colmenarejo, SpainGroup GIAA, University Carlos III of Madrid, Avd. de Gregorio Peces-Barba Martínez, 22, 28270 Colmenarejo, SpainEuropean Organisation for the Safety of Air Navigation (EUROCONTROL), NMD/INF/CNS SASS-C, Rue de la Fusee, 96, 1130 Brussels, BelgiumEuropean Organisation for the Safety of Air Navigation (EUROCONTROL), NMD/INF/CNS SASS-C, Rue de la Fusee, 96, 1130 Brussels, BelgiumEuropean Organisation for the Safety of Air Navigation (EUROCONTROL), NMD/INF/CNS SASS-C, Rue de la Fusee, 96, 1130 Brussels, BelgiumThis paper presents significant enhancements to the vertical reconstruction component of EUROCONTROL’s Surveillance Analysis Support System for ATC Centres (SASS-C). We introduce four key improvements: (1) a novel segmentation algorithm for more precise flight phase identification, (2) an improved invalid height detection process using LOWESS and sliding window analysis, (3) a protection mechanism against simultaneous measurements at the Kalman filter level, and (4) an optimized approach for smooth overshoot correction during segment transitions. These advancements address limitations in the current system, particularly in trajectory segmentation accuracy and robustness against measurement anomalies. Our methodology employs both synthetic and real-world data for comprehensive evaluation, ensuring performance under controlled and operational conditions. The results demonstrate substantial improvements in segmentation precision, outlier detection, and overall trajectory reconstruction quality. The invalid detection algorithm, while incurring a slight computational cost, significantly enhances trajectory accuracy. These enhancements contribute to more reliable air traffic analysis, supporting safer and more efficient airspace management. The paper concludes by discussing potential future work, including the application of machine learning techniques and the extension of these improvements to horizontal reconstruction processes.https://www.mdpi.com/2226-4310/11/11/900trajectory reconstructionvertical segmentationKalman filteringoutlier detectionSASS-CEUROCONTROL
spellingShingle Daniel Amigo
David Sánchez Pedroche
Jesús García
José Manuel Molina
Jekaterina Trofimova
Emmanuel Voet
Benoît Van Bogaert
Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques
Aerospace
trajectory reconstruction
vertical segmentation
Kalman filtering
outlier detection
SASS-C
EUROCONTROL
title Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques
title_full Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques
title_fullStr Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques
title_full_unstemmed Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques
title_short Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques
title_sort enhancing vertical trajectory reconstruction in sass c advanced segmentation outlier detection and filtering techniques
topic trajectory reconstruction
vertical segmentation
Kalman filtering
outlier detection
SASS-C
EUROCONTROL
url https://www.mdpi.com/2226-4310/11/11/900
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AT jesusgarcia enhancingverticaltrajectoryreconstructioninsasscadvancedsegmentationoutlierdetectionandfilteringtechniques
AT josemanuelmolina enhancingverticaltrajectoryreconstructioninsasscadvancedsegmentationoutlierdetectionandfilteringtechniques
AT jekaterinatrofimova enhancingverticaltrajectoryreconstructioninsasscadvancedsegmentationoutlierdetectionandfilteringtechniques
AT emmanuelvoet enhancingverticaltrajectoryreconstructioninsasscadvancedsegmentationoutlierdetectionandfilteringtechniques
AT benoitvanbogaert enhancingverticaltrajectoryreconstructioninsasscadvancedsegmentationoutlierdetectionandfilteringtechniques