Three-Modal Passive Fusion
Architected to detect RF-silent and fibre-tethered platforms through non-RF sensing pathways. Fusion logic determines threat class by cross-validating which modalities trigger — and which don't.
Convolutional neural network trained on propeller acoustic signatures. Detects platforms regardless of RF emissions.
Infrared detection for visual confirmation and operator alerting. Day/night operational capability.
Software-defined radio for Remote-ID and control signal detection. Canadian-IP SDR modules.
Cross-validation logic determines threat classification based on which sensors trigger and which remain silent.
Fibre-tethered platforms and SLAM-autonomous UAS operate without RF emissions. Single-modality RF detection cannot address this threat class.
Fusion logic cross-validates sensor triggers. A platform detected by acoustic but not RF indicates potential fibre-tethered or autonomous operation.
System remains operational if individual sensors are jammed, spoofed, or physically compromised. Architecture designed for node loss, not avoidance.
EchoNet is currently under validation at the Government of Canada CUAS Sandbox 2026 evaluation program. System testing focuses on multi-modal sensor fusion performance in operational conditions.
For evaluation teams, system integrators, and research partners.
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