A new approach to aircraft categorization using machine learning to analyze aircraft behaviour
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-4214.vid The establishment of aircraft categories is a classification technique employed in a variety of aviation disciplines including design and development, certification, ongoing airworthiness, air traffic management, surveillance, and safety analysis. Traditional approaches to aircraft classification rely on manual feature engineering which can be labor-intensive and ineffective for capturing complex patterns. In this paper, a novel approach to aircraft classification using unsupervised machine learning clustering techniques is proposed. The aim of the proposed approach is to be simple in order to be useful and understandable across disciplinary domains; to be scalable to large volumes of air traffic data in order to leverage this data for the purpose of improving the understanding of aircraft behaviours; and to be easily adaptable to future changes in order to account for the evolving technological and operational nature of the airspace environment. The application is based on an adapted version of the k-means algorithm that can automatically group aircraft into clusters based on similarities in features such as position, velocity and acceleration over a period of time. The proposed approach is validated using a real-world air traffic dataset obtained from publicly available ADS-B data, and the results are compared to traditional classification methods from the field of aircraft certification. The findings suggest that leveraging machine learning clustering techniques is a promising approach to aircraft classification, enabling automated and scalable solutions for a variety of applications. The proposed approach has the potential to improve aircraft classification accuracy tailored for specific applications, enhance situational awareness of the airspace environment, and ultimately enhance aviation safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".