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\begin{document}

\title{Separation of Spacecraft Noise from Geomagnetic Field Observations
through Density-Based Cluster Analysis and Compressive Sensing}



\author[1]{Alex Paul Hoffmann}%
\author[2]{Mark B. Moldwin}%
\affil[1]{University of Michigan}%
\affil[2]{University of Michigan-Ann Arbor}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
Spacecraft equipped with magnetometers provide useful magnetic field
data for a variety of applications such as monitoring the Earth's
magnetic field. However, spacecraft electrical systems generate magnetic
noise that interfere with geomagnetic field data captured by
magnetometers. Traditional solutions to this problem utilize mechanical
booms to extend magnetometers away from noise sources. This solution can
increase design complexity, cost, and introduce boom deployment risk. If
a spacecraft is equipped with multiple magnetometers, signal processing
algorithms can be used to compare magnetometer measurements and remove
stray magnetic noise signals. We propose the use of density-based
cluster analysis to identify spacecraft noise signals and compressive
sensing to separate spacecraft noise from geomagnetic field data. This
method assumes no prior knowledge of the number, location, or amplitude
of noise signals, but assumes that they are independent and have minimal
overlapping spectral properties. We demonstrate the validity of this
algorithm by separating high latitude magnetic perturbations recorded by
SWARM from noise signals in simulation and in a laboratory experiment
using a mock CubeSat apparatus. In the case of more noise sources than
magnetometers, this problem is an instance of Underdetermined Blind
Source Separation (UBSS). This work presents a UBSS signal processing
algorithm to remove spacecraft noise and eliminate the need for a
mechanical boom.%
\end{abstract}%



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