Reactive Temporal Logic-Based Precursor Detection Algorithm for Terminal Airspace Operations

Deshmukh, Raj; Sun, Dawei; Kim, Kwangyeon; Hwang, Inseok

doi:10.2514/1.d0182

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…While the precise definition of an air traffic flow depends on the application, it is generally considered a pattern of air traffic in the spatial and, in some applications, temporal dimensions. A large portion of aviation research focuses on the spatial dimension of air traffic flows [11,[29][30][31][32][33][34][35][36][37][38]. However, it is occasionally observed that there are trajectories of flights that do not appear to belong to any specific air traffic flow.Much of the literature related to identifying air traffic flows reports a certain percentage of the trajectories detected as outliers, where these trajectories may be considered spatial anomalies.…”

Section: Background and Motivationmentioning

confidence: 99%

“…to detect a high or low energy landing as anomalous, an anomaly detection algorithm would likely require an entire trajectory's energy profile. As indicated, the metrics selected for energy anomaly detection include SPE, SKE, and STER, as combinations of these metrics have been widely used in aviation energy anomaly detection studies [4,11,28,[30][31][32]53]. To prevent bias in the DBSCAN clustering due to having metrics of varying magnitudes, a normalization is performed prior to applying DBSCAN.…”

Section: Energy Anomaly Detectionmentioning

confidence: 99%

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A clustering-based quantitative analysis of the interdependent relationship between spatial and energy anomalies in ADS-B trajectory data

Corrado

Puranik

Fischer

et al. 2021

Transportation Research Part C: Emerging Technologies

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As air traffic demand grows, robust, data-driven anomaly detection methods are required to ensure that aviation systems become safer and more efficient. The terminal airspace is identified as the most critical airspace for both individual flight-level and system-level safety and efficiency. As such, developing data-driven anomaly detection methods to analyze terminal airspace operations is paramount. With the expansion of ADS-B technology, open-source flight tracking data has become more readily available to enable larger-scale analyses of aircraft operations. This paper makes a distinction between spatial metrics in ADS-B trajectory data and energy metrics derived from ADS-B trajectory data. Motivated by the limited number of approaches that simultaneously consider both spatial and energy metrics, this paper introduces the concepts of spatial anomalies and energy anomalies. In particular, it proposes a novel, unified framework for detection of spatial and energy anomalies in ADS-B trajectory data (and associated derived metrics). The framework consists of three main parts -a data processing procedure, a spatial anomaly detection method, and an energy anomaly detection method. The framework is demonstrated utilizing four months of ADS-B trajectory data associated with arrivals at San Francisco International Airport, and the relationship between the spatial and energy anomalies in this terminal airspace is explored. The results that stem from the implementation of this framework indicate that if an aircraft is spatially not conforming to an identified set of air traffic flows representing standard spatial operations, then this aircraft is more likely to experience non-conformance to standard operations in its energy metrics. Aviation operators, such as air traffic controllers, may benefit from this observation, as it may factor into decision-making in instances where there is the potential to instruct an aircraft to spatially deviate from standard operations. Additionally, this research revealed underlying differences between trajectories that are spatially nominal yet energy-anomalous and those trajectories that are spatially anomalous and energy-anomalous. Focusing solely on energy anomaly detection does not provide insight into potential spatial-related decisions that may have been made to result in off-nominal energy behavior.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Energy Anomaly Detectionmentioning

confidence: 99%

A clustering-based quantitative analysis of the interdependent relationship between spatial and energy anomalies in ADS-B trajectory data

Corrado

Puranik

Fischer

et al. 2021

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…In the work of Li et al [11], the flight data are directly set for clustering and the abnormal flights of takeoffs and landings are detected without prior knowledge. Deshmukh and Hwang [12] and Deshmukh et al [13] propose a logic-based anomaly detection for terminal airspace operations. There are also many works that apply unsupervised learning for anomaly detection in aviation field [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection and Cause Analysis During Landing Approach Using Recurrent Neural Network

Mori

2021

Journal of Aerospace Information Systems

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The stabilized approach is important to avoid aircraft accidents during landing. Although there are many possible factors that can lead to an unstable approach, actions can be taken to mitigate the risk if the reason for unstable approaches is identified properly. However, because unstable approaches are rarely observed, it is difficult to identify the relevant reasons by analyzing unstable approaches only. Instead, the author proposes to identify untypical flights, which are not necessarily categorized into unstable approaches, by using neural network. Using the proposed method, more flights are available to identify the reason of unstable approaches. The stability index is estimated by neural network considering the current flight conditions such as wind and initial deviation. Therefore, untypical flights can be detected by comparing the actual stability index and the estimated stability index. The long-term interaction between the stability index and flight conditions is modeled by using gated recurrent unit. As a result of modeling, some of flights are categorized into untypical flights, and the false localizer beam is observed for most of these flights, which could be a potential hazard for unstable approaches. The proposed approach has a big potential to identify such potential hazards based on historical flight data.

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An interpretable precursor-driven hierarchical model for predictive aircraft safety

Yang,

Yu,

Tang

et al. 2024

Engineering Applications of Artificial Intelligence

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Reactive Temporal Logic-Based Precursor Detection Algorithm for Terminal Airspace Operations

Cited by 8 publications

References 11 publications

A clustering-based quantitative analysis of the interdependent relationship between spatial and energy anomalies in ADS-B trajectory data

A clustering-based quantitative analysis of the interdependent relationship between spatial and energy anomalies in ADS-B trajectory data

Anomaly Detection and Cause Analysis During Landing Approach Using Recurrent Neural Network

An interpretable precursor-driven hierarchical model for predictive aircraft safety

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