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Separation of Spacecraft Noise from Geomagnetic Field Observations through Density-Based Cluster Analysis and Compressive Sensing
  • Alex Paul Hoffmann,
  • Mark B. Moldwin
Alex Paul Hoffmann
University of Michigan

Corresponding Author:aphoff@umich.edu

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Mark B. Moldwin
University of Michigan-Ann Arbor
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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.