Generalized Time-Series Analysis for In-Situ Spacecraft Observations:
Anomaly Detection and Data Prioritization using Principal Components
Analysis and Unsupervised Clustering
Abstract
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.