Data for Enabling In-Situ Magnetic Interference Mitigation Algorithm Validation via a Laboratory-Generated Dataset

Matthew Finley; Allison Flores; Katherine K. Morris; Robert M. Broadfoot; Sam Hisel; Jason Homann; Chris Piker; Ananya Sen Gupta; David M Miles

doi:10.25820/data.007168

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Data for Enabling In-Situ Magnetic Interference Mitigation Algorithm Validation via a Laboratory-Generated Dataset

Matthew Finley, Allison Flores, Katherine K. Morris, Robert M. Broadfoot, Sam Hisel, Jason Homann, Chris Piker, Ananya Sen Gupta and David M Miles

University of Iowa

07/23/2024

DOI: 10.25820/data.007168

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Abstract

Magnetometer measurements are one of the critical components necessary to improve our understanding of the intricate physical processes coupling mass, momentum, and energy within near-Earth space and throughout our solar system. However, these measurements are often contaminated by stray magnetic fields from the spacecraft hosting the magnetic field sensors, and the data often requires the application of interference mitigation algorithms prior to scientific use. Rigorous numerical validation of these techniques can be challenging when they are applied to in-situ spaceflight data, as a ground truth for the local magnetic field is often unavailable. This manuscript introduces and details the generation of an open-source dataset designed to facilitate the assessment of interference mitigation techniques for magnetic field data collected during spaceflight missions. The dataset contains over 100 hours of magnetic field data comprising mixtures of near-DC trends, physically-synthesized interference, and pseudo-geophysical phenomena. These constituent source signals have been independently captured by four synchronized magnetometers sampling at high cadence and combined into 30-minute intervals of data representative of events and interference seen in historic missions. The physical location of the four magnetometers relative to the interference sources enables researchers to test their interference mitigation algorithms with various magnetometer suite configurations, and the dataset also provides a ground truth for the underlying interference signals, enabling rigorous quantification of the results of past, present, and future interference mitigation efforts.

Spacecraft Magnetic Interference

Spacecraft Megnetometer Data

Magnetic Interference Mitigation Dataset

Interference Mitigation Algorithms

Magnetometer Measurements

Ground-Truth Magnetic Field Data

Stray Magnetic Fields

Laboratory-Generated Magnetometer Dataset

Space Physics

Reaction Wheel Magnetic Interference

Synthetic Spacecraft Magnetic Interference

Details

Title: Subtitle: Data for Enabling In-Situ Magnetic Interference Mitigation Algorithm Validation via a Laboratory-Generated Dataset
Creators: Matthew Finley - University of Iowa, Physics and Astronomy
Allison Flores - University of Iowa
Katherine K. Morris - University of Iowa
Robert M. Broadfoot - University of Iowa
Sam Hisel - University of Iowa
Jason Homann - University of Iowa
Chris Piker - University of Iowa
Ananya Sen Gupta - University of Iowa, Electrical and Computer Engineering
David M Miles - University of Iowa, Physics and Astronomy
Contributors: Brian Westra (Data Curator) - University of Iowa, Humanities and Social Sciences/Scholarly Impact
Resource Type: Dataset
Publisher: University of Iowa
DOI: 10.25820/data.007168
Grants: FA9550-21-1-0206, United States Air Force Office of Scientific Research (United States, Arlington) - AFOSR
Partnership for Heliophysics and Space Environment Research, 80NSSC21M0180, National Aeronautics and Space Administration (United States, Washington) - NASA
MAGSTAR: Multi-Mission MAGnetometer Denoising and Sensor Resiliency through STatistical Decomposition and ARtificial Intelligence, 80NSSC23K1295, National Aeronautics and Space Administration (United States, Washington) - NASA
Language: English
Date collected: 05/16/2023
Date published: 07/23/2024
Description methods: The dataset's physical setup includes commercially available magnetoresistive vector magnetometers synchronized and sampled at 200 Hz. A 2-meter cubic 3-axis Merritt coil system simulates large near-DC magnetic fields, while electric motors with ferromagnetic plates simulate spacecraft reaction wheels. The setup allows for the generation of various interference sources representative of spaceflight conditions. The dataset captures near-DC trends, simulated interference, and pseudo-geophysical signals. Stimuli applied to the Merritt coil system and motors generate these signals, providing ground truth for interference and geophysical fields. Data collection details, including applied stimuli and signal characteristics, are provided, ensuring reproducibility and standardization. Data processing involves filtering, truncation, and combination to convert raw data into usable formats. Level 1 processing applies appropriate filtering to each data category, while Level 2 processing combines data subsets to create intervals for interference mitigation algorithm validation. Data is stored in MATLAB v7 format (.mat) and Common Data Format (.cdf) for accessibility.
Academic Unit: Physics and Astronomy; Electrical and Computer Engineering; Humanities and Social Sciences/Scholarly Impact
Record Identifier: 9984623157102771

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Data for Enabling In-Situ Magnetic Interference Mitigation Algorithm Validation via a Laboratory-Generated Dataset

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