On September 23, 2025, Copenhagen Airport shut down for nearly four hours after sightings of large drones near the facility, stranding thousands of passengers. U.S. Northern Command documented over 350 unauthorized drone flights across more than 100 military installations in 2024 alone.
These aren't isolated incidents. In the first quarter of 2025 alone, the FAA recorded 411 illegal drone incursions at U.S. airports — a 25 percent increase over the same period in 2024.
For security decision-makers these drone threats are an existential risk, now! Traditional single-mode detection solutions with manual alarms can't detect these "low, slow and (relatively) small (LSS) drones. However, a new solution combining a trifecta of multiple new developments is now making inroads.
Why Drones Are So Hard to Stop
Modern small unmanned aircraft systems (UAS) present a perfect convergence of technical challenges:
Size and Speed
Low cost, even commercial, drones can travel a kilometer in under a minute with radar cross-sections smaller than birds! Traditional radar-based air defense systems designed for aircraft and missiles struggle to distinguish them from background clutter.
Autonomy
Many drones now operate with reduced or intermittent communication links, making RF link detection unreliable. GPS-guided and pre-mapped autonomous flight means a drone can complete its mission even after losing contact with its operator.
Swarm Tactics
Coordinated multi-drone attacks can overwhelm point defenses. What works against a single intruder fails when facing multiple simultaneous approaches from different vectors.
Environmental Complexity
Defenders must operate in electromagnetically noisy environments such as airports, stadiums, industrial facilities where distinguishing threats from legitimate traffic is extraordinarily difficult.
Legal and Collateral Damage Constraints
Unlike military battlefields, civilian infrastructure such as data centers, power stations, or corporate offices, require non-kinetic solutions that won't endanger bystanders or disrupt critical systems like 911 networks or commercial aviation.
Solutions need to address 3 fundamental challenges: Reliable detection, Identification, Friend/Foe determination. And, these need to happen in real time with low latency and act even when faced with disrupted networks.
The Three Fundamental Challenges
Detection
The Problem: No single sensor reliably detects all drone types in all conditions.
Radar excels at long-range tracking but struggles with small, slow-moving objects near ground clutter. Acoustic sensors work well in quiet environments but are useless in urban noise. RF detectors looking for link connections are blind to autonomous drones. Optical systems require line-of-sight and fail in poor weather.
- Distributed Sensor Networks: Deploying overlapping coverage from multiple sensor types to eliminate blind spots. The principle is redundancy—what one sensor misses, another catches.
- Adaptive Waveform Radar: Software-defined radars that can adjust frequency, pulse width, and processing algorithms in real-time to optimize detection of small targets while filtering clutter.
- AI (Machine Learning): AI models trained on vast datasets of drone signatures (RF, acoustic, radar return) can distinguish between birds, drones, and debris with high confidence, reducing false alarms.
Attribution and Identification
The Problem: Detecting something in the sky is only half the battle. Is it a threat? Who's controlling it? What's its intent?
During December 2023, Langley Air Force Base experienced 17 consecutive nights of drone overflights involving formations as large as 20 feet long, traveling at 100 miles per hour. Despite weeks of investigation by the Pentagon, FBI, and NASA, operators were never identified.
- RF Fingerprinting: Advanced RF signal analysis, including both active sensing and passive sensing techniques can identify specific drone models by their unique electromagnetic signatures such as modulation patterns, frequency hopping sequences, even manufacturing variances in transmitters.
- Behavioral Analysis: AI systems track flight patterns to assess intent. A drone hovering over a substation displays different behavior than one transiting to a delivery location.
- Collaborative Databases: PhoenixAI is working with select partners to explore building collaborative real-time databases that share knowledge, creating an exponential scale of knowledge that enhances our collective defense posture.
Speed of Decision
The Problem: By the time a human operator confirms a threat by cross-referencing radar, RF, and optical alerts, a fast-moving drone has already reached its target!
- Automated Sensor Coordination: When one sensor detects an anomaly, it can automatically direct other sensors to focus on that sector. An RF hit cues optical systems to slew and provide visual confirmation, in real time.
- Edge AI: Edge and on-premises compute make systems robust, eliminating impractical round-trip delays to the cloud or distant data centers, providing real time edge intelligence.
- Predictive Trajectory Modeling: AI systems don't just track where a drone is; they predict where it's going based on velocity, flight dynamics, and local geography, giving defenders precious extra seconds.
PhoenixAI's "3+More" Sensor Fusion Technology
PhoenixAI's CUAS solution solves for the 3 challenges and adds a few more critical real-world dimensions that are necessary for a real world solution.
Advanced RF Technology
Advanced RF Technology has evolved beyond simple signal detection. Software-defined radios can now identify specific drone models, distinguish control signals from video downlinks, and adapt to new communication protocols. New and exciting "passive sensing" senses its physical environment using radio signals perceiving changes, or "disturbances," in the monitored space.
Multi-Processing Edge AI
Edge AI brings machine learning directly to sensors, enabling real-time classification without cloud latency. A radar with embedded neural networks can distinguish a small drone from a bird in milliseconds. These systems learn environmental baselines and can flag genuine anomalies minimizing false positives. Distributed multi-edge processing eliminates single points of failure.
Physical AI
Physical AI represents the next evolution: systems that understand spatial relationships, predict trajectories, and reason about intent. These platforms maintain track continuity across sensor handoffs, fuse multi-modal data into coherent threat assessments, and can recommend or autonomously execute countermeasures based on threat level.
Rapid Interoperability
PhoenixAI's solution not just solves for the core challenges but offers necessary real world features: Easy deployment, interworking with existing or other vendor solutions and contributing to the creation of a robust supply chain. PhoenixAI's "Agentic AI adapters" work with MCP (Model Context Protocol) support to ensure that integration with ecosystem players is rapid and painless, minimizing vendor lock-in and creating architecture flexibility.
What This Means for Decision-Makers
- Detection probability increases while false alarms drop—often by an order of magnitude. Security teams can't afford alert fatigue, and threats can't hide in noise.
- Classification accuracy improves, reducing the risk of engaging authorized aircraft. In environments with commercial drone operations, this discrimination is legally essential.
- Response time decreases, giving operators decision space. Automated sensor cueing means confirmation happens in seconds rather than minutes.
- Scalability becomes practical. Fusion systems can integrate new sensor types as they become available or as threats evolve. Software-defined platforms improve through updates rather than hardware replacement.
- Cost efficiency improves. While fusion systems require higher upfront investment, they eliminate the need for redundant manual monitoring and dramatically reduce false alarm response costs.
Looking Ahead
With major events like the 2026 World Cup and growing threats to data centers that house critical AI infrastructure, the gap between vulnerability and protection is widening.
The drone threat will continue to evolve—AI-piloted systems that adapt to countermeasures, swarms that coordinate to overwhelm defenses, hybrid platforms combining multiple attack vectors. For critical infrastructure, public venues, or military installations, CUAS defenses will need intelligent fusion that sees the complete picture faster and more clearly than ever before.
That future is operational now.
Protect What Matters
PhoenixAI's sensor fusion technology delivers the comprehensive, real-time defense you need against evolving drone threats. Contact us to learn how we can secure your critical infrastructure.