Signal Enhancement for Magnetic Navigation Challenge Problem
- URL: http://arxiv.org/abs/2007.12158v1
- Date: Thu, 23 Jul 2020 17:44:02 GMT
- Title: Signal Enhancement for Magnetic Navigation Challenge Problem
- Authors: Albert R. Gnadt, Joseph Belarge, Aaron Canciani, Lauren Conger, Joseph
Curro, Alan Edelman, Peter Morales, Michael F. O'Keeffe, Jonathan Taylor,
Christopher Rackauckas
- Abstract summary: This paper aims 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 shows 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 neural network.
- Score: 2.1374806859886495
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 data from the
magnetometer on the navigation system 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 magnitude, which is
crucial for navigation, from the total magnetic field magnitude 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 shows 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 neural network. These challenges
offer an opportunity to construct an effective neural network for removing the
aircraft magnetic field from the dataset, using an ML algorithm integrated with
physics of magnetic navigation.
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