Detect, classify, and disrupt drone swarms in real time using neuromorphic RF sensing and adaptive AI. Not a sensor. Not a jammer. The decision layer.
Four-stage pipeline: Detect → Classify → Disrupt → Handoff
Real-time detection across 2.4–6 GHz spectrum. Neuromorphic processing on edge hardware (Jetson Nano). 90%+ classification accuracy on known swarm signatures.
Adaptive jamming on detected frequencies. Link takeover capability. 87% disruption success rate in lab validation. Real-time frequency hopping detection.
Machine learning models trained on real-world drone data. Adaptive threat classification. Swarm behavior analysis. Autonomous response recommendations.
RESTful API for kinetic/non-kinetic system handoff. Integration with existing air defense infrastructure. Modular hardware deployment (hours, not weeks).
Lab and field validation results (Q4 2025 — Q1 2026)
Swarm link disruption in controlled RF environment
Successful command injection in lab test
Multi-band RF denial across common drone comms
Adaptive jamming on detected frequencies. Real-time frequency hopping detection. Power optimization to maximize denial range.
Command protocol analysis. Injection of spoofed control signals. Return-to-base forcing. Lab validated: 6/8 successful attempts.
Selective link disruption. Isolate lead drone from followers. Prevent coordinated attack. Planned field validation: Q2 2026.
18-month evolution from Detection to Autonomous Response
Schedule a technical briefing with our team. We'll walk through disruption proof, integration pathways, and pilot opportunities.
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