Standalone Magnetometry Is the New GPS

· Source: IEEE Spectrum · Field: Technology & Digital — Robotics & Autonomous Systems, Emerging Technologies & Innovation, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Standalone magnetometry is emerging as a robust alternative to GPS, particularly in environments where GPS signals are weak, jammed, or spoofed, such as indoors, underwater, or in conflict zones. Companies like AstraNav, Oriient, and SysNav are now leveraging magnetometers in mobile devices with magnetic map data to provide standalone positioning, moving beyond previous reliance on technologies like Bluetooth or RFID. AstraNav, for instance, secured a US \$1.8 million SBIR grant to demonstrate drone navigation for the U.S. Air Force and partnered with Sonitor for healthcare tracking systems. Advances in signal processing and neural networks have overcome challenges like magnetic interference, enabling precise indoor navigation and even outdoor use without pre-mapping in some cases. The market for indoor positioning and navigation is projected to exceed \$150 billion by 2030, driven by demand from factories, retailers, and defense applications.

Key takeaway

For Robotics Engineers developing autonomous systems for indoor or contested environments, standalone magnetometry presents a viable, infrastructure-free alternative to GPS. You should evaluate its precision and robustness for applications like factory automation or drone navigation, especially where jamming or signal loss is a concern. Consider pilot programs to assess its performance in dynamic, real-world settings, leveraging its potential for on-device computation.

Key insights

Standalone magnetometry offers a robust GPS alternative for precise indoor, outdoor, and contested environment navigation.

Principles

Method

Companies create magnetic maps of environments, then use device magnetometers and advanced signal processing or neural networks for real-time localization, sometimes on-device.

In practice

Topics

Best for: Investor, Entrepreneur, Robotics Engineer, Research Scientist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by IEEE Spectrum.