Aircraft Trajectory Dataset Augmentation in Latent Space

TL;DR

This paper introduces ATRADA, a novel framework that uses a Transformer-based latent space approach combined with PCA and GMM to generate high-quality synthetic aircraft trajectory data for dataset augmentation.

Contribution

The paper presents a new method for aircraft trajectory data augmentation using a Transformer encoder, PCA, and GMM, improving the quality and diversity of synthetic data.

Findings

Effective generation of high-quality synthetic aircraft trajectories.

Outperforms several baseline methods in data augmentation tasks.

Enhances robustness of aircraft trajectory models.

Abstract

Aircraft trajectory modeling plays a crucial role in air traffic management (ATM) and is important for various downstream tasks, including conflict detection and landing time prediction. Dataset augmentation by adding synthetically generated trajectory data is necessary to develop a more robust aircraft trajectory model and ensure that the trajectory dataset is sufficient and balanced. We propose a novel framework called ATRADA for aircraft trajectory dataset augmentation. In the proposed framework, a Transformer encoder learns the underlying patterns in the original trajectory dataset and converts each data point into a context vector in the learned latent space. The converted dataset is projected to reduced dimensions using principal component analysis (PCA), and a Gaussian mixture model (GMM) is applied to fit the probability distribution of the data points in the reduced-dimensional space. Finally, new samples are drawn from the fitted GMM, the dimension of the samples is reverted to the original dimension, and the samples are decoded with a multi-layer perceptron (MLP). Several experiments demonstrate that the framework effectively generates new, high-quality synthetic aircraft trajectory data, which were compared to the results of several baselines.