Aircraft atypical approach detection using functional principal component analysis

Jarry, Gabriel; Delahaye, Daniel; Nicol, Florence; Féron, Éric

doi:10.1016/j.jairtraman.2020.101787

Cited by 31 publications

(27 citation statements)

References 14 publications

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“…Due to the increased prevalence of the HDBSCAN [21] algorithm within the aviation literature [16,22,23], it is selected as the clustering algorithm for which to test the effectiveness of the WED. The HDBSCAN algorithm extends the widely-utilized DBSCAN by converting it to a hierarchical clustering algorithm, where it ultimately extracts a flat clustering (set of clusters without any explicit structure that relates the clusters to each other) based on the stability of the identified hierarchical clusters [24].…”

Section: Methodsmentioning

confidence: 99%

Trajectory Clustering within the Terminal Airspace Utilizing a Weighted Distance Function

Corrado

Puranik

Pinon

et al. 2020

8th OpenSky Symposium 2020

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To support efforts to modernize aviation systems to be safer and more efficient, high-precision trajectory prediction and robust anomaly detection methods are required. The terminal airspace is identified as the most critical airspace for individual flight-level and system-level safety and efficiency. To support successful trajectory prediction and anomaly detection methods within the terminal airspace, accurate identification of air traffic flows is paramount. Typically, air traffic flows are identified utilizing clustering algorithms, where performance relies on the definition of an appropriate distance function. The convergent/divergent nature of flows within the terminal airspace makes the definition of an appropriate distance function challenging. Utilization of the Euclidean distance is standard in aviation literature due to little computational expense and ability to cluster entire trajectories or trajectory segments at once. However, a primary limitation in the utilization of the Euclidean distance is the uneven distribution of distances as aircraft arrive at or depart from the airport, which may result in skewed classification and inadequate identification of air traffic flows. Therefore, a weighted Euclidean distance function is proposed to improve trajectory clustering within the terminal airspace. In this work, various weighting schemes are evaluated, applying the HDBSCAN algorithm to cluster the trajectories. This work demonstrates the promise of utilizing a weighted Euclidean distance function to improve the identification of terminal airspace air traffic flows. In particular, for the selected terminal airspace, if trajectory points closer to the border of the terminal airspace, but not necessarily at the border, are weighted highest, then a more accurate clustering is computed.

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Section: Methodsmentioning

confidence: 99%

Trajectory Clustering within the Terminal Airspace Utilizing a Weighted Distance Function

Corrado

Puranik

Pinon

et al. 2020

8th OpenSky Symposium 2020

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“…The application of subspace-methods as dimensionality reduction techniques are particularly useful when applied to high-dimensional data. Several applications of subspace-methods to aviation exist (see Section 4), including an improved faster method based on Kernel PCA [46] and Functional PCA [52].…”

Section: Subspace-based Methodsmentioning

confidence: 99%

“…Jarry et al [52] propose a method based on FPCA to identify atypical approaches and landings both in post-operational analysis and on-line. The method was tested with track radar data (20,756 records) of landing operations at Paris Charles-De-Gaulle (CDG) airport.…”

Section: Reconstruction-based Approachesmentioning

confidence: 99%

Recent Advances in Anomaly Detection Methods Applied to Aviation

2019

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Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

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“…Previous work [10] has proposed using unsupervised learning to provide post-operational detection of atypical behaviour in the total energy of the aircraft. This methodology proposed by Jarry et al is based on the combination of a sliding window, an information geometry tool called functional principal component analysis and outlier scoring as illustrated in Figure 2.…”

Section: B Previous Related Workmentioning

confidence: 99%

Real Time Aircraft Atypical Approach Detection for Air Traffic Control

Jarry¹,

Delahaye²,

Puechmorel³

et al. 2021

Preprint

Self Cite

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In this paper, a complete tool for real-time detection of atypical energy behaviors of airplanes is presented. The methodology extends in real time an existing offline process using Dubins trajectories as a predictor of the remaining distance to the runway threshold. Two major contributions are presented in this paper. First, a real-time measure of the aircraft energy behaviour is defined, indicating whether the aircraft is in good condition to intercept the extended runway centreline from its current position. Secondly, a 2D trajectory suggestion is given, allowing safe management of the approach path according to atypical criteria of historical data. Finally, this document proposes a comprehensive tool for air traffic controllers, which is a major step forward in understanding, becoming aware of and resolving critical situations that could lead to accidents.

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Aircraft atypical approach detection using functional principal component analysis

Cited by 31 publications

References 14 publications

Trajectory Clustering within the Terminal Airspace Utilizing a Weighted Distance Function

Trajectory Clustering within the Terminal Airspace Utilizing a Weighted Distance Function

Recent Advances in Anomaly Detection Methods Applied to Aviation

Real Time Aircraft Atypical Approach Detection for Air Traffic Control

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