The MagNav project created an open-source Magnetic Navigation Open Challenge to build a machine learning model for removing aircraft electromagnetic noise from the total magnetic field. The trouble is that performing magnetic navigation in real-time is very difficult, especially when modern aircraft are filled with lights, transmitters, computers, and other devices that generate electromagnetic noise and disrupt calculations. One of the central challenges of magnetic navigation is sorting a clean signal through that noise to get an accurate read of the Earth’s magnetic field.Īrtificial intelligence may have finally made that possible.
When mapped, the different levels of magnetization generated by each anomaly can help navigators figure out where they are. One of those methods is the magnetic compass, but anomalies in the Earth’s crust could tell aviators more than just the general direction of north.
#Air navigation gps update
Without GPS, aviators must rely on other ways to update the inertial navigation system, and some of those methods go back centuries. Many aviators today use GPS to update the inertial navigation system and stay on course. But the disadvantage is that the system grows less accurate over time, which can be a problem over long flights.
The advantage of such systems is that they do not depend on external signals, so they cannot be jammed. Inertial navigation systems take an aircraft’s initial position and uses velocity, acceleration, and the laws of physics to determine where the aircraft is at any point in time, McAlpin explained. McAlpin is the Air Force liaison for the Magnetic Navigation (MagNav) project being pursued by the Artificial Intelligence Accelerator, a research pipeline managed by the Air Force and the Massachusetts Institute of Technology. Kyle McAlpin told Air & Space Forces Magazine.
Currently, many military and civilian aviators rely on a combination of GPS and inertial navigation, Air Force C-17 pilot Maj.
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