Platform

Technology

CommCentra AI adds a passive detection layer over existing aviation infrastructure—no aircraft modifications and no change to controller positions. The same architecture covers manned ATC voice and UAS C2 with domain-specific models and thresholds.

Four detection layers

Alerts emphasize consensus across layers to control false positives in safety-critical operations.

1. 01

   RF physical authentication

   ATC

   Enrolled RF fingerprinting for licensed ATC transmitters; rogue SDR and replay signatures surface as hardware-inconsistent.

   UAS / Drone

   Fingerprints operator controllers and telemetry radios; substitution on reconnect is visible before commands execute.

2. 02

   Behavioral & telemetry analysis

   ATC

   Clearances cross-checked against ATIS, flight plans, sector rules, and coordination records—voice alone is insufficient.

   UAS / Drone

   GPS vs IMU / baro / optical flow divergence flags spoofing; hands control to inertial or safe modes when thresholds trip.

3. 03

   AI semantic & voice engine

   ATC

   Phraseology, prosody, and controller-statistical models flag synthetic or cloned speech humans may not hear.

   UAS / Drone

   Operator behavior biometrics (timing, throttle, route habits) that a link hijacker cannot instantly mimic.

4. 04

   Multi-source corroboration

   ATC

   Voice-only clearances triangulated with ADS-B, radar, ACARS/CPDLC where available—single-source voice is treated skeptically.

   UAS / Drone

   Mesh ranging, optical flow, IMU, and registration context must agree; single-source GPS is never enough.