构建6类火箭体光变曲线数据集，助力太空碎片智能识别
RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification
- 基于光变曲线构建标准化火箭体分类数据集
- 涵盖6类火箭体共7080个样本，模型准确率达92%
- 适合航天监测、空间态势感知研究者使用
太空碎片威胁未来航天任务可持续性，亟需可靠、标准化的识别方法。然而，火箭体分类尚缺乏全面基准。本文基于Mini Mega Tortora数据库，构建了包含六类火箭体（CZ-3B、Atlas 5 Centaur、Falcon 9、H-2A、Ariane 5、Delta 4）的光变曲线数据集RoBo6。数据集共含5,676个训练样本和1,404个测试样本，通过重采样、归一化与滤波处理解决数据不一致问题。评估了CNN及基于Transformer的方法，其中Astroconformer表现最佳。该数据集为后续火箭体分类研究提供统一基准。
原文摘要 · Abstract (English)
Space debris presents a critical challenge for the sustainability of future space missions, emphasizing the need for robust and standardized identification methods. However, a comprehensive benchmark for rocket body classification remains absent. This paper addresses this gap by introducing the RoBo6 dataset for rocket body classification based on light curves. The dataset, derived from the Mini Mega Tortora database, includes light curves for six rocket body classes: CZ-3B, Atlas 5 Centaur, Falcon 9, H-2A, Ariane 5, and Delta 4. With 5,676 training and 1,404 test samples, it addresses data inconsistencies using resampling, normalization, and filtering techniques. Several machine learning models were evaluated, including CNN and transformer-based approaches, with Astroconformer reporting the best performance. The dataset establishes a common benchmark for future comparisons and advancements in rocket body classification tasks.
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