Statistical Decomposition and Machine Learning to Clean In-Situ Spaceflight Magnetic Field Measurements

dc.contributor.authorFinley, Matthew G. F
dc.contributor.authorBowen, Trevor A
dc.contributor.authorPulupa, Marc
dc.contributor.authorKoval, Andriy
dc.contributor.authorMiles, David Michael
dc.date.accessioned2023-04-18T18:10:38Z
dc.date.available2023-04-18T18:10:38Z
dc.date.issued2023-03-16
dc.description.abstractRobust in-situ magnetic field measurements are critical to understanding the various mechanisms that couple mass, momentum, and energy throughout our solar system. However, the spacecraft on which magnetometers are often deployed contaminate the magnetic field measurements via onboard subsystems including reaction wheels and magnetorquers. Two magnetometers can be deployed at different distances from the spacecraft to determine an approximation of the interfering field for subsequent removal, but constant data streams from both magnetometers can be impractical due to power and telemetry limitations. Here we propose a method to identify and remove time-varying magnetic interference from sources such as reaction wheels using statistical decomposition and convolutional neural networks, providing high-fidelity magnetic field data even in cases where dual-sensor measurements are not constantly available. For example, a measurement interval from the Parker Solar Probe outboard magnetometer experienced a 95.1% reduction in reaction wheel interference following application of the proposed technique.en_US
dc.description.sponsorshipThe CASSIOPE/Swarm-Echo mission is supported by the European Space Agency’s Third Party Mission Program. This work was supported in part by the US Air Force Office of Scientific Research (FA9550-21-1-0206). Parker Solar Probe was designed, built, and is now operated by the Johns Hopkins Applied Physics Laboratory as part of NASA’s Living With a Star (LWS) program (contract NNN06AA01C).en_US
dc.description.urihttps://www.authorea.com/users/540814/articles/629517-statistical-decomposition-and-machine-learning-to-clean-in-situ-spaceflight-magnetic-field-measurementsen_US
dc.format.extent18 pagesen_US
dc.genrejournal articlesen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m2gj5e-sdqp
dc.identifier.urihttps://doi.org/10.22541/essoar.167898507.75130571/v1
dc.identifier.urihttp://hdl.handle.net/11603/27624
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Goddard Planetary Heliophysics Institute (GPHI)
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.titleStatistical Decomposition and Machine Learning to Clean In-Situ Spaceflight Magnetic Field Measurementsen_US
dc.typeTexten_US

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