基于PCA重构误差的自适应异常检测，实时识别太空任务中的关键事件
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions
- 用增量PCA动态适应数据分布变化，无需预设模型
- 在MMS和THEMIS数据中成功识别日侧/夜侧瞬态现象与过渡层
- 适合资源受限的星载实时分析，可处理多特征时间序列
分析多特征时间序列对太空任务至关重要，有助于高效事件检测，尤其适用于星载实时分析。然而，受限于星载计算资源和数据下行约束，需具备鲁棒性的方法来实时识别感兴趣区域。本文提出一种基于主成分分析（PCA）重构误差的自适应异常检测算法，专为太空任务设计。该算法通过增量PCA动态适应数据分布变化，实现无需预先定义模型即可部署。采用预缩放处理对每个特征进行幅值归一化，同时保留同类特征间的相对方差。实验验证了该方法在识别空间等离子体事件方面的有效性，包括不同空间环境、日侧与夜侧瞬态现象及过渡层，基于NASA MMS任务观测数据。此外，该方法应用于NASA THEMIS数据，成功利用星载可用测量数据识别出日侧瞬态事件。
原文摘要 · Abstract (English)
Analyzing multi-featured time series data is critical for space missions making efficient event detection, potentially onboard, essential for automatic analysis. However, limited onboard computational resources and data downlink constraints necessitate robust methods for identifying regions of interest in real time. This work presents an adaptive outlier detection algorithm based on the reconstruction error of Principal Component Analysis (PCA) for feature reduction, designed explicitly for space mission applications. The algorithm adapts dynamically to evolving data distributions by using Incremental PCA, enabling deployment without a predefined model for all possible conditions. A pre-scaling process normalizes each feature's magnitude while preserving relative variance within feature types. We demonstrate the algorithm's effectiveness in detecting space plasma events, such as distinct space environments, dayside and nightside transients phenomena, and transition layers through NASA's MMS mission observations. Additionally, we apply the method to NASA's THEMIS data, successfully identifying a dayside transient using onboard-available measurements.
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