Three passive modalities. One fusion layer. Every detection gap closed.
The primary detection surface. A CNN trained on rotary-wing propeller signatures identifies platforms regardless of RF emissions. Physical sound cannot be silenced — the most resilient pathway in the stack.
Passive infrared for close-range visual confirmation. Emits nothing. Narrows threat class, supports rules-of-engagement decisions, and works against all platforms including those running RF-silent.
Passive SDR monitors the electromagnetic environment for Remote-ID broadcasts and drone control links. When RF is absent, the fusion core treats silence as data — not a coverage gap.
Determines threat class by cross-validating which modalities trigger and which remain silent. Acoustic without RF indicates autonomous or fibre-tethered operation. Operational under node loss.
EchoNet is architecture-ready for integration with existing C2 frameworks — designed as a distributed sensing extension that feeds into operational data flows without displacing them.
Architecture designed for compatibility with CoT and SAPIENT-type data flow standards. Integration pathways require no modification of upstream command systems.
An API-based data layer enables modular connection to external processing, display, and decision-support systems without proprietary middleware.
EchoNet augments existing sensor coverage as a complementary sensing layer. Deployment requires no replacement or reconfiguration of installed systems.
EchoNet components are at TRL 5 — system-level integration targets TRL 6 at the 2026 Sandbox.
Field validation at CFB Suffield. Architecture-level demonstration under operational conditions.
Integration of field feedback. Sensor fusion algorithm refinement and hardware iteration.
Expanded real-world trials. Mobile platform integration and production architecture design.
Validated across CUAS operational scenarios.