Signal Processing Techniques for Spaceflight Magnetometry: Advanced Algorithms for Boomless Magnetic Field Measurements

Abstract

This dissertation details advancements in spaceborne magnetometry through the introduction of computational algorithms that effectively mitigate spacecraft-generated magnetic interference in magnetometer data. The first contribution of this work is the Underdetermined Blind Source Separation (UBSS) algorithm. This method uses density-based cluster analysis and compressive sensing to identify and separate stray magnetic noise from ambient magnetic field measurements. Traditionally, long mechanical booms are used to distance the magnetometers away from the spacecraft and perform gradiometry. UBSS marks a significant shift from this methodology by enabling the use of lower quality magnetometers with significantly shorter booms, or no boom at all, to achieve high fidelity magnetic field measurements and thereby reduce mission cost and complexity. Notably, UBSS has been selected to be used with the magnetometer payloads of the NASA Lunar Gateway and the Geospace Dynamics Constellation. Building upon the foundation laid by UBSS, the dissertation introduces an integrated noise removal suite that combines the UBSS algorithm with the Quad-Mag CubeSat magnetometer. This integration enables high-fidelity magnetic field measurements on CubeSats without the need for deployable booms. The Quad-Mag with UBSS system broadens the possibilities for magnetometer inclusion in various space missions by reducing size, weight, power, and cost constraints. Another major contribution of this work is the Wavelet-Adaptive Interference Cancellation for Underdetermined Platforms (WAIC-UP) algorithm. Tailored for compact and resource-constrained spacecraft like CubeSats, WAIC-UP employs wavelet analysis to offer a highly efficient solution for magnetic interference removal. This algorithm enables robust magnetic field measurements in space with minimal computational resources, making it an ideal choice for small, resource-limited spacecraft. The low-computational complexity enables potential onboard interference removal for applications such as spacecraft attitude determination. The dissertation culminates in the introduction of the MAGnetic signal PRocessing, Interference Mitigation, and Enhancement (MAGPRIME) library. As an open-source Python library, MAGPRIME integrates a comprehensive suite of advanced noise removal algorithms. It aims to standardize methodologies in magnetic noise removal and stimulate further research. This contribution significantly impacts the space science community by offering novel, efficient, and practical solutions to overcome challenges in spaceborne magnetometry. Collectively, these advancements enable high-fidelity magnetic field measurements on small, low-cost spacecraft, thereby revolutionizing design paradigms and facilitating large constellations for space physics research.