CUAS (Counter Unmanned Aerial Systems): A Complete Guide for Defense & HLS Teams

The Evolving Drone (UAV) Threat

Low-cost, commercially available drones have moved from hobby-related gadgets to battlefield multipliers and tools for hostile actors. Airports, power plants, border zones, and forward operating bases are increasingly reporting incursions and near-misses. In the U.S., the Federal Aviation Administration (FAA) receives 100+ drone-sighting reports near airports each month. In fact, according to Reuters, since 2022, there has been a significant expansion in military drone use. As the threat from malicious drones grows, counter-drone systems are becoming essential for protecting critical infrastructure, military forces, and public spaces.

What is CUAS? A Core Introduction

Counter Unmanned Aerial Systems (CUAS) are layered solutions that detect, identify, track, and mitigate unauthorized or hostile drones. Effective CUAS integrates multiple sensor types and tiered responses, with the goal of delivering mission-appropriate, legally compliant force to protect critical infrastructure, deployed forces, and crowded public spaces. The global CUAS market was valued at $3.75 billion in 2024 and is expected to grow from $4.48 billion in 2025 to $14.51 billion by 2030.  

The Kill Chain: Detect, Track, Defeat

Defense organizations commonly frame CUAS around an end-to-end kill chain:

1. Detect – Discover aerial objects of interest across environments
2. Identify/Classify – Is it a bird, a balloon, or a quadcopter?
3. Track – Maintain custody and predict intent/trajectory
4. Mitigate/Defeat – Apply proportional force (electronic or kinetic) within policy and rules of engagement

Key Technologies CUAS Use for Detection

Modern CUAS blends multiple sensors to reduce false alarms and close gaps:

Radio-Frequency (RF) Detection & Direction Finding
RF sensors monitor the spectrum for drone control links, telemetry, and remote ID beacons. When a signal is recognized, the system can estimate location (and, with multiple sensors, triangulate position) for both the drone and its controller, even before the drone (UAV System) is visible. For example, at a sensitive site, an RF sensor picks up a drone’s control link. The system geolocates the pilot to a parking lot outside the perimeter and simultaneously flags the drone’s approximate location for other sensors to cue on. While RF detection works well for early warning, fully autonomous drones with no active RF link will be difficult to spot.

3D radar for Low-Slow-Small (LSS) targets
Modern short-range radars are tuned to detect small radar-cross-section objects moving slowly and at low altitude. They provide range, azimuth, and elevation, which enables continuous tracking even in rain, fog, or at night. However, without good algorithms, ground clutter and birds can generate false alarms. Pairing with EO/IR and AI reduces these false alarms.

Electro-Optical / Infrared (EO/IR) cameras and PTZ auto-cue
EO/IR provides the visual confirmation operators need before escalating responses. For example, if the camera reveals a payload, the risk assessment and response would be escalated. Integrated systems use radar/RF cues to slew a pan-tilt-zoom camera to the predicted intercept point, then auto-track once the drone is in frame. The technology’s performance depends on lighting, background, and atmospheric conditions.

Acoustic arrays
Microphone arrays can detect the distinctive acoustic signatures of small rotors and estimate location. They are lightweight and useful as a supplemental cue in quiet areas. For example, in a wooded area around a border with limited sight lines, an acoustic node can alert about a “buzzing” signature beyond the line of sight. The alert triggers radar and a PTZ search, shortening the time to visual confirmation. This works well in low-noise environments but wind, traffic, and construction noise can raise false alarms; rain can also significantly degrade performance.

Passive device/protocol analytics
Some systems – such as Wi-Fi, Bluetooth, LTE/5G, Remote ID – passively analyze common protocols to spot drone telemetry, video downlinks, or remote ID broadcasts without transmitting. This can enrich RF detection with device fingerprints and air/ground node correlation. However, these technologies are heavily regulated so their effectiveness varies by vendor support and local legal constraints on monitoring.

Multi-sensor data fusion and AI classification
The command layer fuses tracking data and alerts from radar, RF, EO/IR, and acoustic sensors into a single picture. AI models classify targets (bird vs. drone, fixed-wing vs. multirotor), estimate intent, and prioritize operator attention. This is ideal for reducing false positives, preserving custody through hand-offs, and helping operators make faster decisions. As with all data, quality depends on sensor calibration and well-trained AI models.

Methods of Neutralization  

Mitigation must be proportional, authorized, and safe. In practice, teams escalate from least to most intrusive effects, aiming first to divert or safely land a drone before moving to disable or destroy it. Common neutralization options include:

Electronic (non-kinetic) effects
These methods apply force through the electromagnetic spectrum rather than physical projectiles.

Command-and-Control (C2) link jamming
A jammer disrupts the uplink/downlink between pilot and drone. Most commercial drones then enter a fail-safe return-to-home, auto-land, or hover, depending on their configuration.

GNSS denial jamming
By degrading GPS/GLONASS/Galileo reception, the drone can no longer hold position or navigate, often triggering auto-land or drift. This is useful against semi-autonomous craft that rely on GNSS.

Protocol takeover (link takeover)
Some systems lawfully seize the control link by transmitting a stronger, authenticated signal, allowing responders to execute a controlled landing in a safe zone.

Navigation deception (GNSS spoofing)
Advanced highly-restricted tools transmit false navigation data so the drone believes it’s elsewhere or “herd” it away from a protected area. 

High-Power Microwave (HPM)
Pulsed RF energy can upset or permanently damage onboard electronics across multiple drones at once – especially useful for counter-swarm defense.

Directed-energy effects

High-Energy Laser (HEL)
A narrow beam heats and fails critical components – such as rotors, control surfaces, or sensors – resulting in a controlled crash within a defined footprint.

Kinetic (physical) effects

These methods physically capture or defeat the aircraft.

Net launchers (handheld or turreted)
Fire a weighted net to entangle rotors. Some rounds include a parachute so the captured drone descends slowly.

Interceptor UAS (capture-net or tether)
A defender drone rapidly intercepts and nets the target; tethered variants drag it to a designated drop zone.

Airburst / proximity munitions
Specialized rounds detonate near the target to maximize hit probability while minimizing downrange risk of damage to people or property.

Hard-kill interceptors (missiles or guided projectiles)
Used when the risk is severe (explosive payloads, critical assets). Requires strict safety templates and backstops to manage falling debris.

CUAS Layered Defense Matrix

CUAS in Action: Real-World Scenarios

Critical infrastructure:
Fixed sites such as power stations, refineries, ports, prisons, and stadiums benefit from layered, persistent sensing with pre-defined response playbooks. The goal is to maintain custody on any object of interest, escalate lawfully, and prevent debris or collateral damage within sensitive perimeters. 

Real-world example: Spanish prisons deployed a takeover-based CUAS to stop contraband drones. The system detects, locates, takes control of rogue drones, and lands them in a predefined safe zone to prevent drops of drugs, phones, and cash. 

Deployed forces / forward operating bases (FOBs):
Vehicle- or ship-mounted radar and mast-mounted EO/IR provide early warning and tracking, while electronic attack, directed energy, or kinetic options are held in reserve based on rules of engagement. 

Real-world example: During Operation Rough Rider in 2025, U.S. deployed forces shot down large numbers of Houthi one-way attack drones over the Red Sea/Gulf of Aden. Gen. Erik Kurilla told Congress that aircraft using APKWS laser-guided rockets downed “a little under half” of the drones during the campaign. 

Urban events:
Crowded venues require highly selective effects and precise cueing to avoid downrange risk to people and property. Systems emphasize attribution (who/where is the pilot), visual confirmation, and non-fragmenting mitigation when authorized.

Real-world example: During the Paris 2024 Olympics, French authorities intercepted 53 unauthorized drones near Olympic sites. The security posture included anti-drone units with radar, cameras, and jamming antennas, plus handheld anti-drone rifles; systems were authorized to neutralize drones deemed a threat. Border and coastal security:
Wide, varied terrain calls for distributed sensors that hand off tracking as targets move. Integration with patrol units and airspace managers is essential for timely interception. 

Real-world example: Lithuanian State Border Guard officers used anti-drone equipment to take control of a smuggling UAV and force it to land, recovering a payload of contraband cigarettes. Authorities later reported dozens of similar drone interceptions that year along the Belarus border. 

Navigating Legal and Ethical Hurdles

Counter Unmanned Aerial Systems programs must operate within the law. In the U.S., active mitigation (e.g., jamming, takeover, destruction) is usually limited to specific federal entities. Non-federal operators must navigate detection, reporting, and coordination with relevant authorities and legal advisors. Ethically, programs should prioritize necessity and proportionality: use the least intrusive effect that achieves the safety outcome, minimize collateral hazards, and preserve civil liberties by limiting surveillance, retaining data only as long as needed, and logging all decisions for auditability. 

The Future of Counter-Drone Tech  

The next wave of counter-drone tech will center on tighter multi-sensor fusion powered by edge computing solutions and AI, so operators get faster, clearer “is it a drone?” answers with fewer false alarms. Procurement is also shifting toward low-SWaP, affordable interceptors and auditable governance, an approach that aligns with Maris-Tech’s focus on SWaP-optimized edge video analytics to accelerate detection, classification, and cueing.

Maris-Tech’s products include Jupiter Drones H.264/H.265 codec, an advanced drone-oriented dual-channel H.264/H.265 codec. Jupiter-Drones handles multiple streams simultaneously and supports end-to-end 100msec ultra-low-latency streaming over networks. Jupiter AI integrates a powerful onboard Hailo-8 AI accelerator that enables highly efficient AI features such as detection, classification and tracking and other customer AI processes, with compact form factor and low power consumption for extended professional and tactical operations. 

To experience Jupiter Drones and Jupiter AI supporting CUAS missions, visit us at DSEI (Booth N11-110) September 9-12 in London. Click here to book a meeting.

Frequently Asked Questions