用CycleGAN生成高保真船舶轨迹数据，解决海事数据稀疏问题。
AIS-CycleGen: A CycleGAN-Based Framework for High-Fidelity Synthetic AIS Data Generation and Augmentation
- 基于无配对域转换生成真实船舶轨迹序列
- 生成数据PSNR达30.5，FID为38.9，质量领先
- 适合提升海事预测模型在数据少场景下的表现
自动识别系统（AIS）数据对海事领域感知至关重要，但常面临域偏移、数据稀疏和类别不平衡问题，制约预测模型性能。本文提出一种基于循环一致生成对抗网络（CycleGAN）的鲁棒数据增强方法AIS-CycleGen，专为AIS数据集设计。与传统方法不同，AIS-CycleGen利用无配对域转换生成高质量合成AIS数据序列，无需源-目标成对数据。该框架采用一维卷积生成器结合自适应噪声注入，有效保留船舶轨迹的时空结构，提升生成数据的多样性和真实性。在多个基准回归模型上的实验表明，AIS-CycleGen显著提升模型性能。结果表明，其优于现有基于GAN的增强技术，在测试中达到30.5的PSNR值和38.9的FID分数，验证了其在真实海事智能应用中作为通用、高效数据增强方案的潜力。
原文摘要 · Abstract (English)
Automatic Identification System (AIS) data are vital for maritime domain awareness, yet they often suffer from domain shifts, data sparsity, and class imbalance, which hinder the performance of predictive models. In this paper, we propose a robust data augmentation method, AISCycleGen, based on Cycle-Consistent Generative Adversarial Networks (CycleGAN), which is tailored for AIS datasets. Unlike traditional methods, AISCycleGen leverages unpaired domain translation to generate high-fidelity synthetic AIS data sequences without requiring paired source-target data. The framework employs a 1D convolutional generator with adaptive noise injection to preserve the spatiotemporal structure of AIS trajectories, enhancing the diversity and realism of the generated data. To demonstrate its efficacy, we apply AISCycleGen to several baseline regression models, showing improvements in performance across various maritime domains. The results indicate that AISCycleGen outperforms contemporary GAN-based augmentation techniques, achieving a PSNR value of 30.5 and an FID score of 38.9. These findings underscore AISCycleGen's potential as an effective and generalizable solution for augmenting AIS datasets, improving downstream model performance in real-world maritime intelligence applications.
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