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.

翻译：大型语言模型（LLMs）在航空领域具有变革性潜力，尤其在飞行轨迹重构方面。本文基于LLMs擅长处理序列数据和解构复杂数据结构的特点，对其潜力进行探究。研究采用预训练开源LLM——LLaMA 2模型，重点利用存在现实场景中固有异常性的自动相关监视-广播（ADS-B）数据进行飞行轨迹重构。结果表明，该模型能有效滤除噪声，并估计线性和弯曲飞行轨迹。然而，分析也揭示了模型在处理较长数据序列时面临的挑战，这可能归因于LLM模型的令牌长度限制。研究结论凸显了LLMs在飞行轨迹重构中的应用前景，并为其在航空及运输领域的更广泛应用开辟了新途径。