An Exploratory Assessment of LLM's Potential Toward Flight Trajectory Reconstruction Analysis
This addresses flight trajectory reconstruction for aviation analysis, but is an incremental application of existing methods to a new domain.
This paper investigated using the LLaMA 2 LLM to reconstruct flight trajectories from noisy ADS-B data, finding it proficient at filtering noise and estimating both linear and curved trajectories, though it struggled with longer sequences due to token length limitations.
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.