  Large Language Models (LLMs) hold transformative potential in aviation,
particularly in reconstructing flight trajectories. This paper investigates
this potential, grounded in the notion that LLMs excel at processing sequential
data and deciphering complex data structures. Utilizing the LLaMA 2 model, a
pre-trained open-source LLM, the study focuses on reconstructing flight
trajectories using Automatic Dependent Surveillance-Broadcast (ADS-B) data with
irregularities inherent in real-world scenarios. The findings demonstrate the
model's proficiency in filtering noise and estimating both linear and curved
flight trajectories. However, the analysis also reveals challenges in managing
longer data sequences, which may be attributed to the token length limitations
of LLM models. The study's insights underscore the promise of LLMs in flight
trajectory reconstruction and open new avenues for their broader application
across the aviation and transportation sectors.
