Earlier this year I entered DroneWatch AI in the EUDIS Defence Hackathon in Bucharest. The idea stayed with me, and I have now built it into a working demonstrator. This is what it is, where it stands, and why a software layer, not more hardware, is the interesting part of drone detection.
First, honestly: this is a concept demonstrator, not an operational system. The drone-threat signals are simulated. Two public feeds are live and real: ADS-B air traffic and, since 25 September 2026, network Remote ID, which shows only drones that identify themselves, as context, never as a threat. It does not detect non-cooperative drones. I built it to prove the approach and to start conversations with partners.
The problem: cheap drones, expensive detection
Commercial drones now threaten airports, critical infrastructure, public events and borders. The usual answer is specialised counter-drone hardware, which is expensive, so coverage stays sparse and hard to scale. Meanwhile a single sensor is noisy: radio-frequency alone, acoustics alone or radar alone each throw false alarms, and operators get flooded with unfused, unexplained detections they cannot act on.
The idea: fuse weak signals, do not buy more hardware
Plenty of cheap, distributed sensors already exist. The value is not another sensor. It is the software intelligence layer that makes the ones you have smarter, correlates them, and hands a clear, explainable alert to whoever is on watch.
DroneWatch brings radio, acoustic, telemetry, public-feed and human-report signals into one picture. ADS-B, which shows which flights are authorised, is a live and real feed.
Built to not cry wolf
The most important design choice is false-alarm suppression. Alerts are raised only when independent sources agree, and each alert is explained. An alert that is wrong most of the time gets ignored, and in this domain that is the failure that matters.
Live data, a tactical map, and an AI that explains itself
The demonstrator plots live, real air traffic from a public ADS-B feed on a tactical map, around a chosen protected site, next to the simulated drone signals. Since 25 September 2026 it also reads network Remote ID from Dronetag's public network: an identified drone near the site appears as context and never raises the threat level on its own, because identified is not the same as authorised. Assessments are explained in plain operator language. It runs on infrastructure I own and monitor, the same way I build and run every other custom AI system for founders.
Integration
Integration would be defined in a pilot. It stays strictly defensive: detection and early-warning only, with any response left to a separate, authorised, human-in-the-loop system.
Where it goes next, and who I am looking for
The honest next step is a pilot at one real site, to measure the real-world false-alarm rate, the number that actually matters. From there it depends on a partner and on the required export authorisations.
Export control
DroneWatch AI is controlled as military software, category ML21 of the EU Common Military List, in every configuration. It is not supplied, licensed, transferred or made accessible without the required authorisations. Because of this, no technical detail beyond this description is published. Technical documentation is available to authorised parties once the required export authorisations are in place.
I am looking for serious partners to build this into a fielded system: counter-UAS and sensor companies, systems integrators, and people working on European defence innovation. If you bring the sensors and the accreditation path, I bring the AI fusion layer. My background is as an Army Engineer (Military Technical Academy, Bucharest), so I understand both the operational world and the software. If that is a fit, I would value a conversation. You can also read what a paid AI pilot looks like or hire a fractional AI engineer.
Frequently asked questions
Is DroneWatch AI an operational counter-drone system?
No. It is a concept demonstrator. The drone-threat signals are simulated and it does not detect non-cooperative drones. Two public feeds are live and real: ADS-B air traffic, and network Remote ID, which shows only drones that identify themselves and is treated as context, never as a threat. It exists to prove the fusion approach and to start partner conversations.
How does it reduce false alarms?
Alerts are raised only when independent sources agree, and each alert is explained.
Does it replace radar or RF sensors?
No. It is the software fusion and AI layer above whatever sensors you already have. It makes distributed, mixed sensors smarter and hands an explainable alert to whoever is on watch.
What would it take to field it?
Real sensor integration at one site, a pilot to measure the real false-alarm rate, and a partner who brings sensors and an accreditation path. EUDIS and European Defence Fund calls fund this kind of defence software layer. DroneWatch AI is controlled as military software, category ML21 of the EU Common Military List, so nothing is supplied, licensed, transferred or made accessible without the required authorisations.
Who built it?
Marius Andronie, an Army Engineer who now builds and operates production AI systems. DroneWatch AI began as an entry to the EUDIS Defence Hackathon in Bucharest.
This project was first entered in the EUDIS Defence Hackathon Spring 2026, Romania, part of the EU Defence Innovation Scheme.