# Signal Enhancement for Magnetic Navigation Challenge Problem

arXiv: https://arxiv.org/abs/2007.12158 · HTML: https://mlsanity.com/paper/2007.12158/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Developing a Series of AI Challenges for the United States Department of the Air Force](https://mlsanity.com/paper/2207.07033/) (2022)
- [Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks](https://mlsanity.com/paper/2401.09631/) (2024)
- [Random forests for detecting weak signals and extracting physical information: a case study of magnetic navigation](https://mlsanity.com/paper/2402.14131/) (2024)
- [Maneuver Identification Challenge](https://mlsanity.com/paper/2108.11503/) (2021)
- [NavFormer: IGRF Forecasting in Moving Coordinate Frames](https://mlsanity.com/paper/2601.18800/) (2026)

## Abstract (arXiv)

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.