Magnetometers are essential instruments in space physics, but their measurements are often contaminated by various external interference sources. In this work, we present a comprehensive review of existing magnetometer interference removal methods and introduce MAGPRIME (MAGnetic signal PRocessing, Interference Mitigation, and Enhancement), an open-source Python library featuring a collection of state-of-the-art interference removal algorithms. MAGPRIME streamlines the process of interference removal in magnetic field data by providing researchers with an integrated, easy-to-use platform. We detail the design, structure, and functionality of the library, as well as its potential to facilitate future research by enabling rapid testing and customization of interference removal methods. Using the MAGPRIME Library, we present two Monte Carlo benchmark results to compare the efficacy of interference removal algorithms in different magnetometer configurations. In Benchmark A, the Underdetermined Blind Source Separation (UBSS) and traditional gradiometry algorithms surpass the uncleaned boom-mounted magnetometers, achieving improved correlation and reducing median error in each simulation. Benchmark B tests the efficacy of the suite of MAGPRIME algorithms in a boomless magnetometer configuration. In this configuration, the UBSS algorithm proves to significantly reduce median error, along with improvements in median correlation and signal to noise ratio. This study highlights MAGPRIME’s potential in enhancing magnetic field measurement accuracy in various spacecraft designs, from traditional gradiometry setups to compact, cost-effective alternatives like bus-mounted CubeSat magnetometers, thus establishing it as a valuable tool for researchers and engineers in space exploration and magnetism studies.

Space-based in situ magnetic field measurements are often limited by spacecraft-generated interference, known as stray magnetic fields. These fields, generated by currents from spacecraft subsystems, are frequently several times stronger than the ambient magnetic field signals of interest. To mitigate this, strict magnetic cleanliness, long mechanical booms, and at least two magnetometers are typically necessary to eliminate the spacecraft-generated magnetic interference. When two magnetometers are placed collinearly on a boom, gradiometry can be performed by modeling the spacecraft’s field as a dipole and subtracting it from the magnetometer measurements. However, this technique requires careful preflight characterization of the spacecraft’s magnetic field to determine the dipole coupling coefficients and sufficient boom length. This process is time-intensive, costly, and prone to error due to the changing nature of a spacecraft magnetic field environment in operation. We propose a novel method for in situ calculation of the gradiometric coupling coefficients, called the Reduction Algorithm for Magnetometer Electromagnetic Noise (RAMEN). RAMEN utilizes single-source point analysis and the time-frequency content of the magnetometer signals to identify stray magnetic field signals and calculate the gradiometric coupling coefficients. Through two Monte Carlo simulations, we demonstrate that the RAMEN gradiometry algorithm matches gradiometry with preflight coupling coefficient estimation. Additionally, we apply the RAMEN algorithm to noisy magnetometer data from the Venus Express spacecraft to demonstrate its use. The RAMEN method enhances the fidelity of spaceborne magnetic field observations using gradiometry and reduces the burden of arduous preflight spacecraft magnetic characterization.

Spacecraft magnetic field measurements are frequently degraded by stray magnetic fields originating from onboard electrical systems. These interference signals can mask the natural ambient magnetic field, reducing the quality of scientific data collected. Traditional approaches involve positioning magnetometers on mechanical booms to minimize the influence of the spacecraft's stray magnetic fields. However, this method is impractical for resource-constrained platforms, such as CubeSats, which necessitate compact and cost-effective designs. In this work, we introduce an interference removal technique called Wavelet-Adaptive Interference Cancellation for Underdetermined Platforms (WAIC-UP). This method effectively eliminates stray magnetic field signals using multiple magnetometers, without requiring prior knowledge of the spectral content, location, or magnitude of the interference signals. WAIC-UP capitalizes on the distinct spectral properties of various interference signals and employs an analytical method to separate them from ambient magnetic field in the wavelet domain. We validate the efficacy of WAIC-UP through a statistical simulation of randomized 1U CubeSat interference configurations, as well as with real-world magnetic field signals generated by copper coils. Our findings demonstrate that WAIC-UP consistently retrieves the ambient magnetic field under various interference conditions and does so with orders of magnitude less computational time compared to other modern noise removal algorithms. By facilitating high-quality magnetic field measurements on boomless platforms, WAIC-UP presents new opportunities for small-satellite-based space science missions.

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.