The problem

Traditional radar systems cannot reliably detect small commercial drones — their radar cross-section is too small and they fly below coverage. Recent DoD assessments found 78% of small drone incursions at forward operating bases went undetected by existing radar until visual identification, leaving insufficient warning time. Electronic-warfare systems focus on military frequencies and miss the commercial 2.4 GHz / 5.8 GHz protocols that represent 85% of drone threats in the current battlefield.

How it works

Spectrogram AI continuously captures RF in the bands where commercial drones operate, generates real-time spectrograms with GNU Radio, and runs convolutional neural networks trained on drone communication signatures. When the system identifies drone activity, it alerts operators within 30 seconds through visual, audible, and API channels.

Adaptive learning, not retraining

Adversaries iterate on drone hardware faster than traditional ML retraining cycles allow. Spectrogram AI combines transfer learning, few-shot learning, and online learning so that:

• Minutes — operators define new signatures by frequency, modulation, and timing parameters through the web interface.
• 2–4 hours — few-shot learning generates a detection head from 5–10 labeled signal examples, with auto-validation against historical captures.
• Continuous — operator confirm/reject feedback updates confidence and threshold automatically.

Capabilities

• Real-time detection — alert generation within 30 seconds of drone signal appearance
• Configurable sensitivity — adjustable thresholds for high-threat vs. routine operations
• Multi-band coverage — simultaneous monitoring of 2.4 GHz and 5.8 GHz ISM bands
• Integration-ready — JSON / REST API endpoints for command-system alert distribution
• Air-gap capable — full functionality without external network connectivity

Performance targets

Metric	Target
True-positive rate	> 95%
False-positive rate	< 15% (24 hr)
Detection latency	< 30 s
System availability	> 99.9%
Detection range	2 km+ (optimal); 500 m+ minimum
Install time	< 4 hr at a new location

Architecture

• RF capture — RTL-SDR (Phase I) → MIL-grade SDR (Phase II); 2.4 MSPS per device
• Signal processing — GNU Radio real-time spectrogram pipeline (< 5 s latency)
• Inference — PyTorch CNNs, IEEE DroneDetect baseline + Skycifer field corpus
• Alerting — FastAPI monitoring service with REST and webhook outputs
• Security — AES-256 at rest, role-based access control, comprehensive audit logging

Deployment

Spectrogram AI ships as a stationary installation today, suitable for forward operating bases, airfields, and critical-infrastructure protection. Future variants extend the same detection stack to portable expeditionary kits and to airborne payloads on the SIGINT-X platform.