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