Generalized Time‐Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and Data Prioritization Using Principal Components Analysis and Unsupervised Clustering Article Swipe
Matthew G. Finley
,
Miguel Martínez‐Ledesma
,
W. R. Paterson
,
M. R. Argall
,
David M. Miles
,
J. Dorelli
,
E. Zesta
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2024ea003753
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2024ea003753
In situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near‐Earth space and beyond. However, on‐orbit telemetry constraints can severely limit the capability of spacecraft to transmit high‐cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist‐In‐The‐Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high‐cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low‐cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One‐Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e‐POP/Swarm‐Echo and MMS missions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2024ea003753
- OA Status
- gold
- Cited By
- 3
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402788153
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402788153Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1029/2024ea003753Digital Object Identifier
- Title
-
Generalized Time‐Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and Data Prioritization Using Principal Components Analysis and Unsupervised ClusteringWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-01Full publication date if available
- Authors
-
Matthew G. Finley, Miguel Martínez‐Ledesma, W. R. Paterson, M. R. Argall, David M. Miles, J. Dorelli, E. ZestaList of authors in order
- Landing page
-
https://doi.org/10.1029/2024ea003753Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1029/2024ea003753Direct OA link when available
- Concepts
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Principal component analysis, Anomaly detection, Cluster analysis, Computer science, Time series, Spacecraft, Series (stratigraphy), Pattern recognition (psychology), Anomaly (physics), Data mining, Artificial intelligence, Geology, Machine learning, Physics, Astronomy, Condensed matter physics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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56Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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