The Drone Blockade: Airports Grapple with A Growing Threat

An uninvited buzz of drones in the air is forcing airports worldwide to rethink their approach to security. The implications are serious, impacting everything from airport operational continuity to passenger confidence.

The Escalating Challenge of Drone Incursions at Airports

In December 2018, Gatwick Airport, the UK’s second-busiest, was brought to a standstill by unauthorised drone activity, when reports of drones, sometimes two seen at once, forced an emergency shutdown of its runways. All told, there were 170 reported sightings, with 115 deemed credible, though no definitive photo or video evidence emerged. The shutdown stretched across 36 hours, with intermittent closures pushing the disruption to about 45 hours. This single airport drone disruption resulted in around 1,000 flights cancelled, 110,000 passengers stranded, and significant financial damage, with easyJet alone reporting a £15 million loss. 

This wasn’t an isolated incident. From Dubai to Dublin, and more recently with widespread disruptions in Russia, rogue drones have become a new and unpredictable threat to civilian airports. With the recent success of Ukraine’s Operation Spiderweb,or Israel's Operation Rising Lion,  proving that low-cost drones can deliver a crippling blow deep inside any sovereign nation, those tasked with protecting critical national infrastructure like airports must be rethinking their approach to security.

One thing is clear: the old perimeter fence no longer cuts it. Airports are now forced to develop robust drone defence strategies against threats from above.

Once the preserve of militaries and deep-pocketed hobbyists, unmanned aircraft systems (UAS), or drones, now perform various tasks from delivering packages (at least in pilot schemes) to inspecting wind turbines. The market for unmanned aircraft (UA) and the systems that support them (ground control stations, command and control software, human operators) is soaring. Many analysts project the Unmanned Aircraft System (UAS) industry to be worth $123bn by 2030. However, this rapid growth brings serious challenges for aviation safety. The US Federal Aviation Administration (FAA) has now logged over 2,000 drone incursions near airports since 2021 – almost daily occurrences. And nearly two-thirds of near mid-air collisions at America’s 30 busiest airports feature drones.

The threat is not just from careless amateurs. The potential for drones to be used for reconnaissance, smuggling, or as improvised weapons has security services and international bodies like the International Civil Aviation Organisation (ICAO) sounding the alarm on airport security UAS challenges. “Technological responses to UAS,” ICAO states, are “paramount,” aligning with a clear need for effective counter-UAS solutions for aviation.

Enter Counter-UAS (C-UAS) technology, a field scrambling to keep pace.

Originally born out of military necessity, C-UAS for airports has evolved rapidly from rudimentary jammers to sophisticated, multi-layered defence shields. The question for airports is no longer whether to invest in airport anti-drone systems, but how to choose wisely in a complex and rapidly evolving market.

Spotting the Unwelcome Guest: Airport Strategies for Layered Drone Detection 

Detecting a small, erratically flying piece of plastic and electronics amidst a modern airport's metallic clutter and radio cacophony is no mean feat. No single sensor is a panacea for drone detection at airports. Instead, the watchword is “layers”; a fundamental necessity for a robust layered drone defence airport system. Each layer in a C-UAS airport architecture serves a distinct purpose, building upon the last to create a progressively clearer and more reliable picture of the airspace, thereby enabling a measured and appropriate response to preventing drone incursions at airports.

“What is moving in our airspace? How far away is it? Do we need more information?”

The Outer “Detect” Layer: Early Warning for Airport Airspace Protection

The Outer “Detect” Layer is designed to offer broad coverage and act as an expansive early warning system for airport airspace protection. It must cast a wide net to pick up “rogue traffic” that could be located kilometres from the airport’s core infrastructure. The intelligence gleaned at this stage, whether from radar, RF, or acoustic whispers, serves as the crucial first tripwire. Precision is secondary to the probability of detection; the immediate goal is to sound an alert and narrow the search area for subsequent layers.

Typical Technologies for Airport Drone Detection:

• Long-Range Radar (C-UAS Radar): Systems designed to detect aerial targets at significant distances, providing initial track data like speed, direction and altitude
• Wide-Area Radio Frequency (RF) Detection: Passive systems that scan for drone control signals or telemetry over a broad area. This can sometimes detect a drone controller being activated even before the drone itself is airborne or has travelled far.
• Acoustic Tripwire: Passive systems of sensitive microphones that “see with sound” over an area as wide as the microphone coverage. Acoustic detection is relatively low-cost (potentially utilising pre-existing airport microphones with adaptation) and effective for spotting drones that might slip past radar (due to low altitude or stealthy design) or remain invisible to RF scanners because they are RF-silent, navigating autonomously or using unconventional command links such as fibre-optic tethers.

“Is it a bird or a drone? What kind of drone? Where's it going?"

The Middle “Track and Classify” Layer

After the outer layer flags a potential threat, the middle layer locks on to refine the track and start classification. It distinguishes, for example, a drone from a bird; with deeper analysis, it can even tell the sound, RF or flight signature of a small fixed-wing drone from that of a multirotor. It is here that machine learning technologies for C-UAS become invaluable.

Typical Technologies for Drone Tracking and Classification:

• Shorter-Range, Higher-Resolution Radar: Radars (e.g., X-band, Ku-band, often with micro-Doppler capabilities) providing more detailed information about a target's size, speed, and movement characteristics to aid classification.
• RF Analysis & Fingerprinting: More detailed analysis of detected RF signals to identify the specific drone model, protocol, or even unique identifiers, comparing against a library of known drone signatures.
• Initial Slew-to-Cue Optical Systems (EO/IR C-UAS): Electro-optical (EO) and Infrared (IR) cameras can be automatically directed ("slew-to-cue") by radar or RF detections to attempt an early visual lock on the target.
• Acoustic Beamforming and Signal Processing: If triggered, this layer uses advanced techniques like beamforming to focus on the cued location, enhancing the ability to accurately locate the sound source. It then applies intensive AI-driven signal processing and signature analysis to classify the drone more precisely and extract key acoustic characteristics.
• Machine Learning: AI algorithms sift through the incoming flood of multi-sensor data—analysing radar returns, RF emissions patterns, sound signatures, and early EO/IR glimpses—to extract and highlight key characteristics.

“Is it carrying a visible payload? Is it behaving erratically? Is it on a direct path to a critical area? “

The Inner “Identify and Assess” Layer

Building upon the characteristics initially flagged, the Inner Layer is dedicated to Positive Identification, Tracking, and Comprehensive Threat Assessment. This layer provides the definitive confirmation needed to escalate to a response, definitively dismissing false alarms or confirming UAS threats to airports.

Typical Technologies for Final Threat Confirmation:

• High-Magnification EO/IR Camera Systems: Powerful zoom cameras (visible light and thermal) provide clear visual imagery for human operators or AI-driven visual analytics to confirm the object is a drone, identify its type, and look for modifications or payloads.
• Acoustic Sensors (Situational): In quieter airport zones or during lulls, acoustic sensors might help confirm a drone's presence or even type by its sound signature, especially if other sensors are obscured.
• Data Fusion & AI Analytics: Command and Control (C2) software for C-UAS plays a critical role, fusing data from all previous layers (radar track, RF signature, visual appearance, acoustic data) with contextual information. AI algorithms assess flight patterns and compare them against known threat profiles.

“What do we do now?”

The Response Layer: Action and Neutralisation

Once the Inner Layer has provided positive identification of an unauthorised drone and a comprehensive assessment of its characteristics and potential threat level, the C-UAS moves into the critical Response Layer. This is where all preceding efforts culminate in a decision and, if necessary and authorised, an action for airport drone mitigation.

The Pivotal Role of C2 Software and Situational Awareness:

At the heart of this layer is the Command and Control (C2) software. Having processed and fused data, the C2 system provides security operators with enhanced situational awareness. This includes:

• A Unified Operating Picture (UOP): Displaying all relevant drone information (location, path, speed, altitude, type, characteristics, payload, threat level) on a clear interface.
• Real-time Visualisation and Alerts: Enabling operators to see threats evolve and receive immediate, prioritised alerts.
• Decision Support: Advanced C2 systems offer tools highlighting threats against critical infrastructure, suggesting Standard Operating Procedures (SOP), or appropriate response options based on the scenario and pre-defined Rules of Engagement (ROE), a feature increasingly adapted from the defence sector for the unique pressures of airport drone defence.
• Information for Coordination: The C2 platform serves as a central hub for rapid information sharing with airport management, Air Traffic Control (ATC), law enforcement, and national security agencies.

Disable. Disrupt. Neuatralise: Evaluating Airport Drone Mitigation Strategies

Detection, however sophisticated, is but half the battle when considering how to protect airports from unauthorised drones. Once an unauthorised drone is confirmed, what then? The options for mitigation strategies for drones at airports are fraught with difficulty. Grounding all flights, as seen in the Gatwick drone incident, is financially ruinous, millions of dollars per hour for a major hub. Yet the alternative, a drone colliding with an aircraft, is unthinkable.

There are plenty of neutralisation technologies, each with its own drawbacks in the sensitive airport ecosystem:

• Drone Capture Systems: Net guns, fired from the ground or other drones, offer a dramatic solution but risk falling debris.
• Kinetic C-UAS Solutions: Projectiles or kamikaze interceptor drones are generally unsuitable for civilian airspace due to high collateral damage risks.
• Electronic Warfare (Non-Kinetic C-UAS):
  • RF Jamming: Can sever drone control links but risks crippling aircraft navigation/communication systems if not precisely targeted.
  • GPS Spoofing: Tricking the drone carries similar interference risks.
  • Directed Energy Weapons (DEW): High-energy lasers or microwaves require extreme caution to avoid harm to pilots or electronics.
  • Cyber Takeover: Exploiting vulnerabilities to hijack the drone offers a controlled capture but requires specific intelligence.

Crucially, in most Western jurisdictions, the authority to deploy such countermeasures rests not with airport authorities but with federal law enforcement or security agencies. The legal implications of C-UAS at airports are significant, necessitating a carefully choreographed dance between detection on-site and response from officially sanctioned actors.

How can I get peace of mind? Will insurers help me cover the cost of business disruption?

Insurance and the Business Case for Airport C-UAS

Installing a full counter-drone shield is a significant investment. A layered system, potentially incorporating radar, RF detection, acoustic sensors, thermal cameras, and the C2 software that fuses them, can run into the millions for a large hub.

Upfront costs for civilian C-UAS hardware for municipal-level airspace have been estimated at around $5 million, with substantial ongoing annual subscription or maintenance costs that can be a considerable percentage of the initial outlay. On paper, that’s a hefty line item. In practice, it can be small next to the financial blast radius of a single drone-induced shutdown.   

Gatwick’s 2018 incident starkly illustrates this. The 33-hour closure grounded over 1,000 flights, generating an estimated £50 million in airline claims and a direct loss to the airport of £1.4 million. This disruption cost is many times the potential price of a robust C-UAS stack. With direct aircraft operating costs for some major airlines around $100 per minute (approximately £75-£80, based on 2023 U.S. carrier data), even a brief precautionary runway stop due to a drone sighting can quickly accumulate significant costs across multiple affected flights. Preventing even one major disruption, or a costly false alarm that leads to an operational pause, can heavily offset the C-UAS investment.   

Airports aren’t the only ones doing the maths; their insurers are, too. Aviation insurance programmes already bundle substantial third-party liability and business-interruption (BI) cover. Following high-profile disruptions like Gatwick, insurer scrutiny of airport drone risk mitigation strategies has understandably intensified, and leaders like Willis Tower Watson announced a “drone disruption aviation action plan” and published some supporting materials to help airports better understand C-UAS. The direction of travel is this: Underwriters are increasingly looking for evidence of effective "drone-intrusion controls" during renewals.   

While the market is still evolving, best practices for airport C-UAS are emerging, often emphasising principles like multi-layered detection, capabilities for identifying drone characteristics (such as through Remote ID where feasible and legally implemented ), and well-rehearsed incident response plans. Airports demonstrating a comprehensive approach to managing drone risks, including effective C-UAS deployment, are likely to be viewed more favourably by underwriters. 

Though specific, standardised premium credits across the market are not yet widely documented, a strong risk management posture is a key factor in insurance discussions. Any potential reduction in premiums would, of course, improve the C-UAS business case. For an airport with a multi-million-pound annual insurance bill, even a modest percentage consideration could represent a significant sum, helping to offset the C-UAS system's ongoing operational expenses.   

This all points towards greater integration of C-UAS as a critical safety and security layer. Innovative insurance concepts, such as parametric "runway-closure" covers triggered by verified disruptions, are being discussed as potential future solutions for non-damage business interruption, though widespread piloting for drone-specific events is not yet confirmed. 

Airports that invest proactively in effective C-UAS not only shrink their operational risk and public relations exposure but also strengthen their overall risk profile. While transforming the C-UAS line item into an immediate profit centre solely through insurance rebates might be optimistic, the primary ROI lies in preventing catastrophic losses and ensuring operational continuity.  
 

An Ever-Evolving Aerial Arms Race

The regulatory environment is, like the technology itself, in flux. ICAO, the FAA in America, and EASA in Europe are all working on frameworks, but harmonisation has been slow, and national laws add further layers of complexity. Airports must navigate this legal maze carefully, while keeping tabs on headlines coming out of Russia, Ukraine, Israel and Iran. The rules and regulations may change quickly.

Looking ahead, the cat-and-mouse game between drone capabilities and C-UAS countermeasures will continue. AI and machine learning will make detection smarter and responses more autonomous. Defences against drone swarms are being developed. The quest for safer, more precise non-kinetic effectors will intensify. And as legitimate drone traffic grows under new Unmanned Traffic Management systems, C-UAS will need to seamlessly distinguish the welcome from the unwelcome.

The challenge for airports is clear: to invest, adapt, and collaborate. The buzzing in the air is more than just a nuisance; it is a persistent feature of the modern aviation landscape. Ensuring it remains manageable requires vigilance, innovation, and recognition that in the 21st century, airport security extends well beyond the runway.

Interested in AI's potential to enhance Counter-UAS? Get in touch.