Robust 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.
In-situ magnetic field measurements are critical to our understanding of a variety of space physics phenomena including field-aligned currents and plasma waves. Unfortunately, high-fidelity magnetometer measurements are often degraded by stray magnetic fields from the host spacecraft, its subsystems, and other instruments. One dominant source of magnetic interference on many missions are reaction wheels - spinning platters of varying rates used to control spacecraft attitude. This manuscript presents a novel approach to the mitigation of reaction wheel interference on magnetometer measurements aboard spacecraft where multiple magnetometer sensors are deployed. Specifically, multichannel singular spectrum analysis is employed to decompose multiple time series simultaneously. A technique for automatic component selection is proposed that classifies the decomposed signals into common geophysical signals and disparate locally generated signals enabling the robust estimation and removal of the local interference without requiring any assumptions about its characteristics or source. The utility of this proposed method is demonstrated empirically using in-situ data from the CASSIOPE/Swarm-Echo mission, and a data interval with near-constant background field was shown to have its local reaction wheel interference reduced from 1.90 nT RMS, for the uncorrected outboard sensor, to 0.21 nT RMS (an 89.0\% reduction). This technique can be generalized to arrays of more than two sensors, and should apply to additional types of magnetic interference.