Identification and Removal of Reaction Wheel Interference From In‐Situ Magnetic Field Data Using Multichannel Singular Spectrum Analysis

Abstract

In order to observe and study the magnetic fields near Earth, high-quality magnetic field measurements are necessary. However, the spacecraft carrying magnetic field sensors are often magnetically noisy-they produce stray magnetic fields that contaminate the measurements. One dominant noise source on many spacecrafts are the systems that control the orientation of the spacecraft. This manuscript presents a novel approach to the suppression of magnetic interference caused by these systems, improving the quality of acquired data. Specifically, a technique for the simultaneous separation of multiple measurements into physically meaningful components is combined with an automated component selection technique. This allows for a high-quality estimate of the spacecraft noise to be generated and subsequently removed from magnetic field measurements, greatly improving the data quality. For example, an interval of data captured by the CASSIOPE/Swarm-Echo satellite was shown to have its local interference reduced by an average of 89%.

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

“…Historically, this technique has seen success in a wide range of fields from climatology (Chen et al., 2013; Vautard & Ghil, 1989) to economics (Hassani & Thomakos, 2010; Hassani et al., 2010). Recently, the multivariate extension of SSA (MSSA) has been used to simultaneously decompose time‐series measurements from a pair of satellite‐mounted magnetometers into robust, physically meaningful components corresponding to the near‐DC trend, interesting geomagnetic phenomena, and local magnetic interference (Finley et al., 2023).…”

“…Further mathematical details and results for the application of this dual‐sensor magnetometer denoising technique can be found in Finley et al. (2023).…”

“…A threshold parameter (ɑ) is defined by the user to determine the minimum correlation value that is tolerable in the denoised output. Further mathematical details and results for the application of this dual-sensor magnetometer denoising technique can be found in Finley et al (2023).…”

Enabling Boomless CubeSat Magnetic Field Measurements With the Quad‐Mag Magnetometer and an Improved Underdetermined Blind Source Separation Algorithm

“…However, obtaining accurate measurements of the ambient magnetic field in space can be challenging due to stray magnetic fields from spacecraft subsystems. Spacecraft subsystems, including solar panels, magnetorquers, batteries, and propulsion systems, generate stray magnetic fields that interfere with measurements of the ambient magnetic field (Finley et al., 2023). For instance, the GOES‐16 spacecraft employs electric propulsion‐based thrusters that generate about 20 nT of stray magnetic field noise (Califf et al., 2020).…”

“…Imajo et al (2021) apply Independent Component Analysis to separate noise signal based on their statistical independence, however this method can only separate a limited number of noise signals. Finley et al (2023) applied multivariate singular spectrum analysis to separate out signal components in the time domain. This method has minimal assumptions about signals present, but picking the correct components to reconstitute the natural magnetic field signal is tricky and prone to error.…”