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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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