DroneTector
Product & Competitive Intelligence
Detects, tracks, and classifies hostile drones in real time using radar, cameras, and acoustics.
Company Overview
Develops millimeter-wave radar and multi-sensor fusion systems powered by ML to detect, track, and classify hostile drones and swarms in real time for airports, critical infrastructure, and defense.
Competitive Advantage & Moat
Product Roadmap & Public Announcements
DroneTector builds a suite of sensors including their millimeter-wave radar, purpose-built for the hardest targets. Millimeter-wave radar, multi-sensor fusion (radar, camera, acoustic), distributed detection network, swarm-tracking. Partnerships with UK MoD DASA, NATO DIANA, Royal Academy of Engineering. Pilot deployments at airports and critical infrastructure. Won Converge Challenge 'Best Pitch.'
Signals & Private Analysis
Micro-Doppler signature classification using deep learning. Edge-deployed inference for low-latency detection. Acoustic ML for drone fingerprinting. NATO DIANA provides allied defense test ranges. Future SaaS 'airspace monitoring as a service' offering.
Product Roadmap Priorities
ML-powered micro-Doppler radar signature classification that distinguishes hostile drones from birds, clutter, and other airborne objects in real time.
It teaches the radar to tell the difference between a delivery drone and a seagull by learning the unique "fingerprint" of how each object's spinning parts reflect radar waves.
It's like teaching a bouncer to recognize troublemakers not by their face, but by the way they walk through the door.
Ensemble learning-based multi-sensor fusion that combines radar, camera, and acoustic ML outputs into a single, high-confidence threat assessment.
It cross-checks what the radar sees, the camera spots, and the microphone hears — like getting three independent witnesses to agree before sounding the alarm.
It's like a doctor who doesn't just rely on one test — they combine your X-ray, blood work, and symptoms before making a diagnosis.
Lightweight, edge-deployed computer vision model (YOLO-based) for real-time detection and tracking of small, fast-moving drones in complex visual environments.
It gives every security camera a pair of AI-powered binoculars that can instantly spot and lock onto a tiny drone against a busy sky — without needing a cloud connection.
It's like giving every security camera the reflexes of a fighter pilot and the patience of a birdwatcher — but it never blinks and never needs coffee.
Company Overview
Key Team Members
- Dr. Matthew Moore, Co-Founder & CEO
- Dr. Thomas Doherty, Co-Founder & CTO
- Dr. Jordina Frances de Mas, Co-Founder & COO
Founder Force Multiplier
Three PhDs from world-class institutions: Dr. Matthew Moore (millimeter-wave radar, University of St Andrews), Dr. Thomas Doherty (optical and laser technologies, University of Oxford, Royal Academy of Engineering Enterprise Fellow with a decade of experience), and Dr. Jordina Frances de Mas (automated reasoning and ML, University of St Andrews). Purpose-built ML-native counter-drone hardware that generalist defense contractors cannot easily replicate.
Funding History
- 2023-2024 | DroneTector founded; initial R&D.
- 2024 | UK MoD DASA programme.
- 2024-2025 | NATO DIANA and Royal Academy of Engineering support.
- 2025 | Won Converge Challenge 'Best Pitch'; early pilot deployments.
- 2026 | Accepted into Y Combinator W26 batch.
Competitors
- Radar: Robin Radar, Echodyne, Fortem Technologies.
- RF/Multi-Sensor: Dedrone (Axon), DroneShield, Sentrycs.
- Acoustic: Squarehead Technology.
- Defense Primes: Thales, Leonardo, Hensoldt.
- AI Startups: Iris Automation, D-Fend, SkySafe.
