Hovering recognition by ADS-B data mining

Bian, Lei; Liu, Yu; Zhan, Yi; Hongwu, Tang; Bo, Manhui; Zhang, Linfeng; Luan, Kun; Yin, Xinyan; Ding, Jun

doi:10.1109/iccasit50869.2020.9368614

Cited by 1 publication

(1 citation statement)

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“…Subsequently, these data slices are labeled with correct flight phases by Boolean reasoning, decision trees, or fuzzy logic (24)(25)(26). Many other studies related to ADS-B identification data mining focus primarily on traffic flow classification, specific maneuver pattern recognition, and collision behavior detection (28)(29)(30)(31)(32). Therefore, developing a data-driven approach will facilitate the quantification of operations estimates for GA airports (33).…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Framework for Classification of Flight Phases of General Aviation Aircraft

Zhang

Mott

2022

Transportation Research Record: Journal of the Transportation R

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Flight data mining enables airport owners, operators, and governmental entities to explore more intelligent management strategies; in particular, cost-effectively obtaining accurate operational data is beneficial for general aviation (GA) airports and their associated communities. The current data collection modus operandi, however, does not meet future needs, as aircraft operations are counted manually or estimated by sampling methods. The increasing traffic flow and limited available personnel at most GA airports make it unrealistic to continue using traditional methods to analyze aircraft operational statistics; therefore, a customized approach is needed to address this problem. Since different flight phases have different levels and types of impact on the environment, acquiring information related to the duration of each flight phase at the airport and within its surrounding airspace is critical to the assessment of emissions and noise pollution from aircraft. The primary goal of the research is to provide quantified inputs for the environmental evaluation model, such as the Aviation Environmental Design Tool (AEDT). This paper demonstrates a programmed framework that successfully achieves satisfactory performance in solving flight phase identification problems by testing the synthetic flight data as well as validating the empirical Automatic Dependent Surveillance-Broadcast (ADS-B) data. The experimental results suggest that the proposed methods achieve promising classification accuracy, leading to feasible deployment in airport operations.

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