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Session B10: Magnetic and Gravity Anomaly-Based Navigation 2

The Space-Weather Floor for MagNav: When Crustal Signals and Disturbances Overlap
Manoj Nair and Arnaud Chulliat, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder
Location: Ballroom E
Date/Time: Wednesday, Jun. 3, 11:30 a.m.

Passive magnetic navigation (MagNav) exploits spatially varying crustal magnetic anomalies as a reference for positioning in GPS-denied environments. Although the crustal field is static on operational timescales, airborne MagNav inherently converts spatial magnetic structure into a time-varying signal through platform motion. Consequently, onboard magnetometers observe a composite temporal signal in which crustal information and space-weather-driven magnetic disturbances are superimposed.

This presentation examines the resulting spectral overlap from a system perspective. For typical aircraft speeds and altitudes, the apparent temporal frequencies associated with crustal anomalies frequently coincide with the dominant spectral content of magnetospheric and ionospheric current systems. During moderate to severe geomagnetic activity, external magnetic variations -ranging from approximately 50 nT to more than 1,000 nT - can equal or exceed the amplitudes of crustal anomalies used for navigation. In this regime, conventional frequency-domain filtering is ineffective, as it cannot separate the navigation signal from the disturbances without degrading both.
We then discuss a mitigation approach based on geomagnetic field modeling. Modern geomagnetic field models provide reliable estimates of the internal geomagnetic field and dominant external field contributions from diverse sources, including satellite and ground-based magnetic observations. Rather than treating space weather as an unstructured noise source, these models enable estimation of the expected disturbance environment along a flight trajectory, allowing definition of a bounded and predictable “space-weather floor” for MagNav performance. We further discuss validation of these models against independent scalar datasets and examine their implications for operational MagNav performance.

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