-- PNI Sensor will present new research at the 2026 Joint Navigation Conference, highlighting a field demonstration of its NaviCube compact multi-node magnetometer array and processing architecture for magnetic anomaly-based navigation.
The presentation, titled “Field Demonstration of a Multi-Node Magnetometer Array Enabling Vector-Based Magnetic Navigation Using Reverse EMM Correlation,” is scheduled for Session B9, Magnetic and Gravity Anomaly-Based Navigation 1, at the 2026 Joint Navigation Conference. The work is authored by Jay Trojan and George Hsu, CTO of PNI Sensor, with Noah Jarrah, AMC C-17 Pilot.
Magnetic anomaly-based navigation, or MagNav, has traditionally relied on scalar measurements of Earth’s crustal magnetic field and highly surveyed regional maps. PNI Sensor’s presentation describes how NaviCube is designed to extend MagNav by enabling both conventional scalar magnetic navigation and vector-based magnetic navigation using reverse Enhanced Magnetic Model (EMM) correlation.
NaviCube employs a three-dimensional cubic array of spatially distributed vector magnetometers to directly observe magnetic field gradients and higher-order spatial structure. By using a multi-node architecture, the system is designed to detect and remove hard-iron and soft-iron platform magnetic interference, producing Earth-field magnetic measurements consistent with MagNav requirements even on magnetically unclean platforms.
The presentation will include real field data derived from ground-vehicle measurements and describe a reverse-lookup EMM localization approach that showed promising results. This approach will provide a pathway for localization in areas that have not yet been fully surveyed and mapped for crustal field anomalies, helping bridge the gap while traditional magnetic anomaly map databases continue to mature.
“Traditional magnetic navigation systems often depend on highly surveyed environments and clean magnetic conditions,” said George Hsu, CTO of PNI Sensor. “NaviCube advances magnetic anomaly-based navigation by combining distributed vector magnetometers to deliver more reliable field measurements, even in magnetically challenging platforms. Its compact multi-node architecture enables improved interference rejection and greater navigation robustness.”, said George Hsu, CTO of PNI Sensor.
The research reflects PNI Sensor’s broader focus on precision navigation, magnetic sensing, sensor fusion, adaptive navigation, and magnetic interference rejection for defense and commercial applications. As positioning, navigation, and timing systems face increasing demands in GPS-denied, GPS-degraded, and magnetically complex operating environments, PNI Sensor continues to advance low-SWaP magnetic sensing and navigation technologies intended for real-world platforms.
PNI Sensor’s presentation is part of the conference’s magnetic and gravity anomaly-based navigation program, which explores complementary and alternative PNT approaches for autonomous systems and other mission-critical applications.
Presentation Details
- Conference: 2026 Joint Navigation Conference
- Session: B9, Magnetic and Gravity Anomaly-Based Navigation 1
- Title: “Field Demonstration of a Multi-Node Magnetometer Array Enabling Vector-Based Magnetic Navigation Using Reverse EMM Correlation”
- Authors: Jay Trojan, George Hsu, PNI Sensor; Noah Jarrah, AMC C-17 Pilot
- Primary author: George Hsu, PNI Sensor
About PNI Sensor
PNI Sensor is a positioning and navigation technology company providing magnetic sensing, sensor fusion, inertial navigation, tracking, and targeting solutions for defense and commercial applications. With decades of experience in magnetometry, precision location, and real-world sensor integration, PNI Sensor develops low-SWaP sensors, modules, and algorithms designed to deliver reliable navigation and positioning data in demanding environments, including situations where GPS cannot be used or cannot be relied upon.
For more information, contact www.pnisensor.com
Contact Info:
Name: Becky Oh
Email: Send Email
Organization: PNI Sensor
Website: https://www.pnisensor.com
Release ID: 89193640
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