# Structure-Aware Unsupervised Anomaly Detection for Spacecraft Telemetry with Adaptive EVT Thresholding

arXiv: https://arxiv.org/abs/2609.10017 · HTML: https://mlsanity.com/paper/2609.10017/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Builds on (most cited first, 2 of 2)

- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry](https://mlsanity.com/paper/2406.17826/) (2024)

## Abstract (arXiv)

Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements are rarely met in practice. We propose an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning. The approach combines incremental monthly retraining, statistical model selection, and adaptive Extreme Value Theory (EVT) thresholding for false alarm control. On the ESA Anomalies Dataset (ESA-AD), it achieves F0.5=0.700 on Mission~1 and F0.5=0.698 on Mission~2 under strict chronological evaluation.