JuliaCon 2023

Knowledge-Informed Learning in MagNav.jl for Magnetic Navigation
07-28, 14:30–15:00 (US/Eastern), 32-D463 (Star)

The Earth’s crustal magnetic field is a powerful tool for navigation as an alternative to GPS. MagNav.jl is an open-source Julia package containing algorithms for both aeromagnetic compensation and navigation. Alongside baseline algorithms, such as Tolles-Lawson, this package enables multiple scientific machine learning approaches for compensation. This talk will cover some of these techniques and advanced use cases for MagNav.jl in navigation.


Building on previous talks given at JuliaCon 2021 and 2022 that covered the basics of airborne magnetic navigation (MagNav) and MagNav.jl, this talk will expand on the development of scientific machine learning approaches enabled in its recent version 1.0 release. At a high level, MagNav.jl provides tooling to compensate for an aircraft’s magnetic field, removing most of the corruption to enable comparing magnetometer readings with detailed maps of the Earth’s magnetic field. It can then use the resultant signal, alongside other readings, as input to a navigation filter, such as an extended Kalman filter, in order to estimate position. Julia is integral to our research in this field due to its specialties in automatic differentiation, ease of neural network construction, and its performance. This talk will showcase the ease with which state-of-the-art compensation models (Tolles-Lawson) can be mixed with machine learning to reduce the required training data and enhance magnetic compensation accuracy.

Jonathan Taylor is an Associate Staff at MIT Lincoln Laboratory working on the DAF-MIT AI Accelerator's Magnetic Navigation project. His research interests are primarily centered on Machine learning in novel sensor environments.

B.S. from UW-Madison MechE, M.S. and PhD from MIT AeroAstro. Interested in sustainability, transportation (especially aviation), and the Julia programming language. Former NSF GRFP fellow. Private pilot.