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