Towards unified Geophysical Data Requirements for Magnetic Navigation (MagNav)

This paper initiates a community dialogue on standardized geophysical data requirements for Magnetic Navigation by distinguishing between operational and R&D needs, and proposing specific recommendations such as merged datasets, localized uncertainty estimates, and designated test ranges to overcome current deployment barriers.

The Gist

The Big Picture: A New Compass for a GPS-Free World

Imagine you are driving a car, but your GPS suddenly stops working. Maybe someone is jamming the signal, or maybe you are underwater where GPS signals can't reach. You need a backup way to know exactly where you are.

For thousands of years, sailors used the Earth's magnetic field (like a giant, invisible compass) to find their way. Today, scientists are trying to bring that back, but with super-advanced technology. This is called Magnetic Navigation (MagNav). Instead of just knowing "North," modern MagNav systems measure tiny, unique bumps and dips in the Earth's magnetic field to pinpoint a location with extreme precision.

However, there is a problem: To use this system, you need a perfect map of these magnetic bumps. Currently, the maps we have are like a patchwork quilt made by different people using different rulers, different colors, and different rules. Some parts are blurry; some parts are missing.

This paper, written by experts at SandboxAQ, is a call to action. They are saying: "We have the technology to navigate without GPS, but we need better, standardized maps to make it work for everyone."

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The Two Main Groups: The Drivers and the Mechanics

The paper explains that there are two different groups of people who need these maps, and they need them in different ways. The authors argue that we often mix these two groups up, which causes confusion.

1. The Drivers (Operational MagNav)

Who they are: Pilots, ship captains, and military commanders who are actually flying or sailing right now.
What they need: A single, unified, reliable map.

• The Analogy: Think of this like a Google Maps app on your phone. When you are driving, you don't want to see the raw data from the surveyors who built the road, or the satellite photos from 10 years ago. You just want one clear, smooth map that tells you exactly where you are right now.
• Key Needs:
  • One Global Map: No switching between different map files when crossing state or ocean borders.
  • Uncertainty Estimates: The map needs to say, "I am 99% sure this bump is here," or "I'm only 50% sure because the data is old." This helps the navigation computer know how much to trust the map.
  • 3D Data: The map needs to work at different heights (flying high vs. flying low), not just on the surface.
  • No "Filtering": The map must show the magnetic field exactly as a sensor would see it, without scientists smoothing it out or removing "noise" (like magnetic interference from power lines), because the navigation system needs to see the real world.

2. The Mechanics (MagNav R&D)

Who they are: Scientists, engineers, and researchers building the next generation of navigation systems.
What they need: Raw ingredients and the recipe book.

• The Analogy: Think of this like a chef in a test kitchen. They don't just want the finished cake; they want the raw flour, the eggs, the original recipe notes, and the history of how the dough was mixed. They need to see the "messy" data to figure out how to make the cake (the navigation system) better.
• Key Needs:
  • Raw Data: Access to the original survey lines and unprocessed files.
  • Metadata: Detailed notes on how the data was collected (e.g., "We flew at 500 feet on a Tuesday," or "We removed solar storm data").
  • Searchable Database: A giant library where they can search for specific types of data (e.g., "Show me all magnetic surveys over the ocean from 2020").

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The Three Big Problems to Fix

The authors identify three specific things that need to change to make MagNav work for everyone.

1. The "Patchwork Quilt" Problem (Data Cohesion)

Right now, magnetic maps are scattered. One company has a map of the Midwest, another has a map of the ocean, and they don't fit together perfectly.

• The Fix: We need a single, merged global map. Imagine if you had to stitch together a quilt yourself every time you wanted to go on a trip. It's too hard. We need one giant, pre-stitched quilt that covers the whole world so the "Drivers" can just use it immediately.

2. The "Blurry Photo" Problem (Resolution)

Some maps are high-definition (like a 4K photo), while others are pixelated (like a low-res thumbnail). The current global maps often smooth out the high-definition areas to match the low-resolution ones.

• The Fix: Keep the highest possible resolution. If a part of the world has great data, don't blur it just to match a bad part. Let the system zoom in on the sharp parts. Also, the map needs to be "queryable," meaning the computer can ask, "What is the magnetic field at this exact spot?" and get an answer instantly.

3. The "Missing Layer" Problem (Core Field Modeling)

The Earth has a magnetic field generated by its core (like a giant bar magnet inside). Scientists usually subtract this "core" field to see the smaller "crustal" bumps that help with navigation.

• The Fix: The current standard model (called the World Magnetic Model) stops too early. It's like trying to listen to a song but the radio cuts off the last few notes. The authors suggest upgrading this model to include one more "layer" of detail (degree 13). This ensures that when we subtract the core field, we don't accidentally remove important magnetic signals needed for navigation.

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The "Test Track" Idea

Finally, the paper suggests we need MagNav Test Ranges.

• The Analogy: Think of these like NASCAR test tracks. Before a new car is sold to the public, it needs to be tested on a specific, controlled track where the conditions are known and perfect.
• Why we need them: We need specific geographic areas where we have both the high-quality "Driver" map and the raw "Mechanic" data. This allows researchers to test their systems and prove they work before they are used in real life.
• Where? These tracks should be in different places (land, sea, different altitudes) and should be easy to access without getting stuck in heavy air traffic or military restrictions.

Summary: What is the Paper Asking For?

The authors are asking the government and data providers to stop treating magnetic maps like scattered scientific notes and start treating them like critical infrastructure.

They want:

1. Standardized Maps: One clean, global map for pilots and ships.
2. Raw Data Access: The messy, detailed data for scientists to improve the tech.
3. Better Models: Upgrading the math behind the maps to be more precise.
4. Test Tracks: Designated areas to prove the technology works.

If we do this, MagNav can become a reliable backup for GPS, ensuring that even if the satellites go down, we can still find our way.

Technical Summary

Technical Summary: Towards Unified Geophysical Data Requirements for Magnetic Navigation (MagNav)

Problem Statement
The paper addresses the critical need for resilient Positioning, Navigation, and Timing (PNT) alternatives to GPS/GNSS, particularly in environments where signals are degraded, denied, or spoofed (e.g., underwater, urban canyons, or conflict zones). While Magnetic Navigation (MagNav) has demonstrated sufficient maturity for operational deployment—evidenced by recent real-time flight trials on C-17 and C-130J aircraft and partnerships with Airbus—the technology's scalability and reliability are currently bottlenecked by the state of available geophysical data.

Existing magnetic anomaly datasets, largely developed for natural resource exploration, academic research, or hazard forecasting, often lack the specific characteristics required for navigation. These datasets frequently suffer from:

• Inconsistency: Varied coordinate systems, processing pipelines, and file formats.
• Resolution Mismatches: Global models often downsample high-resolution regional data, losing critical short-wavelength signals needed for precise positioning.
• Lack of Uncertainty Quantification: Many maps lack 3D uncertainty estimates, which are essential for sensor fusion algorithms (e.g., Kalman filters) to weigh magnetic measurements against inertial data.
• Processing Artifacts: Data is often processed for specific industry goals (e.g., Reduced-to-Pole for mineral exploration) rather than preserving the raw signal as it would appear to a navigation sensor.

Methodology and Approach
The authors, drawing on extensive operational experience from SandboxAQ's flight trials (including C-17, C-130J, and Beechcraft Baron campaigns), analyze the gap between current geophysical data products and the requirements for MagNav. The paper does not present new experimental flight data but rather synthesizes empirical findings from past trials to define a set of requirements.

The methodology involves:

1. Use Case Segmentation: Distinguishing between Operational MagNav (deployment on devices requiring robust, unified, queryable datasets) and MagNav R&D (requiring access to raw, high-resolution, and metadata-rich data for algorithm development).
2. Gap Analysis: Comparing existing public and proprietary datasets (e.g., EMAG2v3, NAMAM, WDMAM, NOAA HDGM) against the identified operational needs.
3. Requirement Formulation: Proposing specific data properties, formats, and processing standards tailored to each use case.
4. Model Recommendation: Analyzing the mathematical compatibility between current core field models and magnetic anomaly calculations.

Key Contributions and Recommendations

The paper proposes a structured framework for future geophysical data requirements, categorized by use case:

1. Operational MagNav Requirements
For on-device deployment, the paper recommends a single, cohesive, globally merged dataset with the following properties:

• Dataset Cohesion: A unified global map rather than fragmented survey files to support transoceanic and cross-regional missions.
• Coordinate System: Standardization on WGS-84 or EGM2008 to ensure interoperability.
• Data Fields: Latitude, Longitude, Altitude, Magnetic Anomaly Field magnitude, and 3D Uncertainty Estimates. The authors emphasize that uncertainty must be queryable at the specific altitude of the platform, not just at sea level, to support Kalman filter updates.
• Resolution: Maximum available spatial resolution without over-smoothing. The dataset should support non-uniform grids or equivalent source models to allow users to interpolate data to their specific altitude without downward continuation instabilities.
• Processing: Preservation of the "true" signal as measured by a sensor. The paper explicitly advises against "Reduced-to-Pole" transformations or heavy filtering of "cultural artifacts" (human infrastructure), which are common in resource exploration but detrimental to navigation matching.
• Format & Scalability: Non-proprietary formats and scalable APIs that allow systems with Size, Weight, and Power (SWaP-C) constraints to load only relevant sub-regions of the global model.

2. MagNav R&D Requirements
For research and development, the focus shifts to access and transparency:

• Access to Raw Data: Availability of original survey data, including raw flight lines and intermediate processing steps, to allow researchers to test algorithm robustness against merging artifacts.
• Metadata & Documentation: Comprehensive records of processing history (e.g., solar weather removal, core field models used).
• Searchable Database: A global, searchable repository indexed by flight line spacing, altitude, year, and uncertainty to facilitate mission planning.
• Vector Data: While scalar data is the current operational baseline, the paper identifies vector magnetic anomaly data as a priority for R&D test ranges to assess future performance gains.

3. Core Field Model Expansion
A specific technical recommendation is the expansion of the World Magnetic Model (WMM) to spherical harmonic degree 13. Current WMM versions extend only to degree 12. Since magnetic anomaly maps are typically calculated by removing a core field model to degree 13, using a degree-12 WMM for MagNav leaves a residual signal unaccounted for, degrading navigation accuracy. Aligning the WMM to degree 13 ensures consistency between the core field model and the anomaly maps.

4. Establishment of MagNav Test Ranges
The paper advocates for the creation of designated "MagNav Test Ranges." These regions would concentrate high-quality, multi-altitude, and multi-modal (scalar/vector, land/sea) datasets. These ranges are intended to:

• Facilitate side-by-side validation of operational datasets against raw survey data.
• Support testing in diverse environments (urban, oceanic, transition zones).
• Enable testing of unmanned aerial systems (UAS) in low-altitude airspace where regulatory restrictions often hinder testing.
• Provide empirical evidence to feed back into future standards development.

Results and Claims
The paper claims that MagNav technology has reached a maturity level where it can serve as a primary navigation method in contested environments, citing successful real-time trials on military aircraft. However, the authors assert that the data infrastructure has not yet caught up to the sensor technology.

The primary result of this work is the identification of specific, actionable requirements to bridge this gap. The authors argue that without a unified, uncertainty-aware, and high-resolution global dataset, MagNav cannot scale effectively for global commercial and defense use. They posit that the current fragmentation of data products forces developers to perform manual, error-prone merging and processing, which is unsustainable for operational systems.

Significance
The paper positions itself as a catalyst for community dialogue between the geophysics, navigation, and defense sectors. Its significance lies in:

• Defining the "Data Gap": Clearly articulating that the barrier to MagNav adoption is no longer sensor capability but data quality and standardization.
• Bridging Communities: Differentiating the needs of operational users (who need a "black box" solution) from researchers (who need "glass box" transparency), a distinction often blurred in current discussions.
• Strategic Alignment: Proposing concrete steps (WMM expansion, Test Ranges, unified datasets) that government agencies and data providers can implement to enable Assured PNT.
• Operational Reality: Grounding recommendations in real-world flight trials rather than theoretical models, ensuring the requirements are practical for deployment.

The authors conclude that active collaboration between the Armed Services, Intelligence community, and industry is essential to prioritize these data requirements, ultimately transitioning MagNav from a demonstrated capability to an operationally assured navigation infrastructure.