Videos

Low-cost handheld detection of non-cooperative UAS with edge machine learning

A pocket-sized passive RF detector designed to identify today’s consumer drone threats across tactical, civilian and infrastructure settings

The drone threat has changed. The risk no longer comes primarily from rare, high-end military systems, but from low-cost consumer drones that are easy to buy, assemble and deploy. These devices can appear unexpectedly in sensitive environments, creating security concerns for military operators, law enforcement, critical infrastructure and public events. In this video, we introduce a proof-of-concept passive RF drone detector that combines low-cost hardware with lightweight AI to provide adaptable, wide-spectrum detection in a compact form factor.

Videos

Low-cost handheld detection of non-cooperative UAS with edge machine learning

A pocket-sized passive RF detector designed to identify today’s consumer drone threats across tactical, civilian and infrastructure settings

The drone threat has changed. The risk no longer comes primarily from rare, high-end military systems, but from low-cost consumer drones that are easy to buy, assemble and deploy. These devices can appear unexpectedly in sensitive environments, creating security concerns for military operators, law enforcement, critical infrastructure and public events. In this video, we introduce a proof-of-concept passive RF drone detector that combines low-cost hardware with lightweight AI to provide adaptable, wide-spectrum detection in a compact form factor.

Our approach focuses on detecting the radio frequency emissions from a drone’s command and control link. Using an off-the-shelf spectrum analyser, RF signals are captured and converted into spectrograms—visual representations of frequency over time. These are analysed using a convolutional neural network (CNN), chosen for its strength in image classification tasks.

The model is intentionally lightweight, requiring less computational power than a typical mobile phone, enabling deployment on low-spec hardware. A two-stage scanning process—broad low-resolution sweeps followed by targeted high-resolution analysis—delivers wide spectral coverage without the need for expensive, high-bandwidth equipment.

Beyond established spectrogram-based detection, the system introduces a scalable synthetic data pipeline. With just a single recording of a new drone’s control link, large volumes of representative training data can be generated. This allows the detector to adapt rapidly as new drone models emerge, including those with previously unseen signal signatures.

The platform is designed for multiple deployment scenarios:

  • Tactical environments: Ruggedised hardware, external antenna options and configurable interfaces for operational control
  • Civilian use: Discreet, power-bank-style form factor with vibration alerts for unobtrusive awareness
  • Fixed installations: Power over Ethernet (PoE) variant for integration into prisons, power grids, stadiums and other security networks

By combining passive RF monitoring, efficient AI processing and modular hardware design, the system offers a low-cost and adaptable response to an increasingly accessible drone landscape.

Key Takeaways:

  • Consumer-grade drones now represent the dominant security risk
  • Passive RF detection targets drone command and control emissions
  • Spectrogram analysis with lightweight CNN models enables low-power deployment
  • Two-stage spectrum scanning balances wide coverage with focused precision
  • Synthetic data generation allows rapid updates for new drone types
  • Modular design supports portable, tactical and fixed-site applications

Watch the video to see how this compact detection platform is reshaping drone awareness across defence, security and civilian environments.

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Last Updated
December 15, 2025

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