  Harnessing the magnetic field of the Earth for navigation has shown promise
as a viable alternative to other navigation systems. A magnetic navigation
system collects its own magnetic field data using a magnetometer and uses
magnetic anomaly maps to determine the current location. The greatest challenge
with magnetic navigation arises when the magnetic field measurements from the
magnetometer encompass the magnetic field from not just the Earth, but also
from the vehicle on which it is mounted. It is difficult to separate the Earth
magnetic anomaly field, which is crucial for navigation, from the total
magnetic field reading from the sensor. The purpose of this challenge problem
is to decouple the Earth and aircraft magnetic signals in order to derive a
clean signal from which to perform magnetic navigation. Baseline testing on the
dataset has shown that the Earth magnetic field can be extracted from the total
magnetic field using machine learning (ML). The challenge is to remove the
aircraft magnetic field from the total magnetic field using a trained model.
This challenge offers an opportunity to construct an effective model for
removing the aircraft magnetic field from the dataset by using a scientific
machine learning (SciML) approach comprised of an ML algorithm integrated with
the physics of magnetic navigation.
