Student builds mmWave radar for asbestos detection

// 3h agoTUTORIAL

Student builds mmWave radar for asbestos detection

Gauthier Lechevalier spent six months building a hardware startup around an FMCW mmWave radar designed to detect asbestos in European buildings. By leveraging capon beamforming to process chirp echoes and feeding the data into a neural network, the radar classifies material surfaces to identify hazardous layers without physical intrusion.

// ANALYSIS

–Applying mmWave radar and neural networks to non-intrusive asbestos detection is a highly innovative solution to a pervasive health hazard.
–Rapid prototyping with off-the-shelf boards (IWRL6432, ESP32) demonstrates a lean approach to hardware development.
–The project underscores the difficulty of securing funding for deep-tech hardware startups compared to software companies.
–Combining advanced DSP (FMCW, beamforming) with edge AI for material classification represents a compelling use of modern embedded systems.

// TAGS

radarrfdspembeddedbeamforminghardwarellm

DISCOVERED

3h ago

2026-06-30

PUBLISHED

6h ago

2026-06-30

RELEVANCE

8/ 10

AUTHOR

GL26

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Student builds mmWave radar for asbestos detection