Cross-Aircraft Flight Phase Classification Using ADS-B Data and Transfer Learning
Name
Kiefer_Cross-Aircraft Flight Phase Classification.pdf
Description
Main report
Size
306 KB
Format
Adobe PDF
Checksum (MD5)
0e1b8a4bf1068c0491369c53f4f5c602
Author(s)
Kiefer, Jacob
Alemany, Sheila
Date Issued
March 20, 2026
Abstract
Flight phase identification (FPI) approaches that
apply traditional machine learning techniques are expensive to
scale, difficult to generalize across platforms, and frequently
unavailable in permissive or distributed training environments.
We propose a scalable, data-driven pipeline for automatic FPI
using open-source Automatic Dependent Surveillance-Broadcast
(ADS-B) data, with an emphasis on cross-aircraft generalization
through transfer learning. Leveraging ADS-B telemetry from
USAF Initial Flight Training aircraft, a neural network classifier
is trained on Diamond DA-20 flight data and evaluated on Texan
T-6 aircraft under zero-shot and fine-tuned transfer learning
conditions. We describe a robust ADS-B preprocessing pipeline
integrating digital elevation model (DEM) data, a data labeling
strategy using unsupervised learning, and a transfer learning
approach enabling adaptation across aircraft types with limited
labeled data. Our results demonstrate that transfer learning significantly
improves classification accuracy for flight phases with
limited data, highlighting the potential of ADS-B-based models
to support scalable, behavior-aware airspace intelligence across
heterogeneous fleets and permissive environments. This research
advances FPI capabilities for USAF training analysis and broader
operational priorities in autonomy, situational awareness, and
data-driven decision support.
Subjects
ADS-B, flight phase detection, trajectory analysis, transfer learning, aviation analytics
MIT Department
Lincoln Laboratory
Persistent DSpace Link
