Both the war in Ukraine and the conflict in the Middle East have changed the way war is waged overnight. The rapid proliferation of drones, or rather unmanned aerial vehicles (UAVs), has transformed them into significant threats to civilian infrastructure worldwide. Throughout the European Community, we have witnessed and suffered this through hybrid warfare operations orchestrated by the enemies of Western Europe.
Critical infrastructure such as power grids, oil refineries, logistics centers, airports, and data centers are increasingly vulnerable to attacks, surveillance, or service disruptions caused by the use of drones.
Drone capabilities are advancing at an astonishing pace, facilitated by available technology and the commercial appetite of manufacturing companies, many of them based in China. Every three or four months, new generations appear, improving their range, speed, autonomy, and resistance to interference (read: electronic warfare).
The problem for security and defense forces is not simply locating and neutralizing a drone. The goal is to develop a capability that can safely detect, identify, track, and respond within the constraints of the civilian environment, and then adapt as the threat, or threats, evolve.
Effective defense has transformed into a systems engineering problem encompassing sensors, communications, command and control, legal authority, security policies, and continuous improvement.
Therefore, anti-UAS (counter-unmanned aerial systems) systems should be viewed as an integrated set of techniques rather than a single tool, with detection, tracking, and identification as fundamental functions that must work together.
The civilian environment
Defense operations against drones, in both the military and civilian spheres, are subject to different regulatory frameworks, even though both involve significant and complex risks. In military environments, the spectrum of permissible responses may be broader due to the nature of armed conflict. However, in the civilian sector, this permissiveness does not exist.
In civilian contexts, the repercussions of an error not only include the safety of people, but also exposure to legal liabilities, the disruption of operations, and the erosion of public trust.
Electronic countermeasures must be objective of a thorough analysis and precise delimitation are crucial to avoid interference with essential communications. Furthermore, systems must be able to efficiently manage false alarms, preventing response fatigue.
A Practical Framework
Currently, most anti-UAS programs roughly follow the following operating protocol:
- Detection: Initial detection of an aerial anomaly.
- Tracking: Continuous estimation of position and trajectory.
- Identification: Classification, intent assessment, and confidence scoring.
- Decide: Rule-based escalation with documented authority.
- Identification:
- Response: Safe and legal intervention options appropriate for the scope of operation.
- Evaluation: Confirmation of the outcome and management of the consequences.
- Learning: Updating models, policies, and operating assumptions.
The challenges of early detection and warning
As we have seen above, early drone detection is a complex issue, especially in urban and industrial environments. Therefore, a single system will not be sufficient to ensure detection, nor to generate a response proportionate to the detected threat.
A serious defense policy is one that invests in sensor fusion, as this reduces false alarms and generates a coherent picture for tracking and eliminating the threat. The European Commission’s Joint Research Centre has published specific work on detection, tracking, and identification concepts, as well as on data fusion in counter-UAS contexts, reinforcing the need for systems to integrate multiple techniques rather than relying on a single sensor.
Radar remains fundamental for long-range detection, especially against larger fixed-wing drones. The limitation lies not only in the radar cross-section but also in the radar horizon and interference. As is well known, low-altitude flights can go undetected behind terrain, buildings, vegetation, or industrial structures. This is one of the reasons why low-altitude threats are repeatedly cited as one of the greatest detection challenges for traditional systems.
In the civilian sector, radar is valuable insofar as it buys time. Time means distance from the attacking vehicle. If a site only detects a target a few kilometers away, response options are quickly reduced and safety constraints prevail.
Radio frequency detection is valuable when drones emit known command and control or telemetry signals. Its effectiveness decreases when drones are autonomous, follow pre-planned routes, or emit few signals. Experience explicitly indicates that UAS can operate without active radio frequency signals, which underscores the need for layered detection rather than relying solely on radio frequency.
Electro-optical and Infrared (IR) sensors facilitate visual confirmation, classification, and post-incident verification. They also present challenges related to weather, line-of-sight limitations, and a shorter range than radar, so they should be considered confirmation and tracking sensors. In modern drone detection systems, EO and IR are usually combined with radar and RF rather than used individually.
Video cameras allow for visual identification and recording of evidence. However, their detection is limited by line of sight, lighting, weather conditions, and an effective operating range of less than 100 meters.
New detection systems
Acoustic sensors can help overcome shortcomings in low-altitude detection, or detection without a frequency signal, and provide another classification method. They work by detecting the high frequencies produced by drone motors and propellers, and report the presence of a drone in the area. Their limitations are range and noise. Industrial environments, railway corridors, and urban soundscapes can reduce performance. Even so, acoustic sensors are often useful as a close layer in a broader topology, especially when combined with EO confirmation.
LiDAR (Light Detection and Ranging) sensors are also currently being proposed to improve drone detection through high-resolution 3D scanning. In practice, its usefulness is limited by a short effective range, a narrow field of view, and its sensitivity to weather conditions such as fog, rain, and dust. Covering large perimeters with LiDAR is also expensive. Therefore, these technologies are better suited for specific or complementary roles than as primary detection layers.
Identification Challenges
In civilian situations, identification marks the boundary between security operations and legal action. A detected tracking device does not yet constitute a threat. Threat assessment must consider the type of object, its behavior, the potential risk of the payload, and the consequences of attacking the wrong target.
Identification operates in adverse conditions, where acoustic signals can be falsified, visual confirmation can be affected by weather, and radio frequency indicators may be unavailable due to autonomy. Therefore, effective identification depends on Multiple detection methods. It must support operator confidence, legal defense, and post-event review.
Proportional Response
It is common to refer to the response as “soft takedown” and “hard takedown.” These terms are imprecise for civilian contexts, as the real issue is controlled risk.
1. Response in the Electromagnetic Spectrum
Electronic measures can disrupt navigation or control links. They are limited by regulation, proximity to critical communications, and the possibility of unintended consequences. They can also compromise autonomy.
If electronic effects are part of a civilian strategy, their use must be clearly defined and carefully controlled. Directional application and conflict resolution are essential to avoid non-interference. desired, and deployment must be supported by documented authority.
Effective use also requires coordination in communications with the desired parties to ensure that critical services are not interrupted.
2. Kinetic Interception
Physical interception can be effective, but it presents the risk of damage from the wreckage and debris of the downed object; therefore, it requires strict safety zones. Furthermore, it demands highly reliable identification.
In civilian environments, the safest physical options often involve interception concepts that control the fate of the downed drone’s wreckage rather than simply destroying the target. However, depending on the size of the target, the response could be ballistic, which involves calculating and controlling the point of impact of the munition used.
Various types of weapons and methods have been tested, including 40mm rubber bullets, baseballs, and glass marbles (most failed even at close range), shotguns (the target must be at close range), crossbows (failed), and water hoses (only partially effective at very short range). In many of these tests, the drones suffered significant collateral damage and still managed to stay airborne, albeit sometimes erratically.
Within this type of interception, and considering the proportionality of the response, the following options are among the most effective:
Compressed gas (LPG) launchers that fire a net to envelop and capture a drone at a distance of up to 100 meters. Portable devices with a much shorter range can be used. Aerial “hunter” drones are emerging that fire compressed cartridges with nets to envelop a drone in flight and bring it down.
Anti-drone net shotgun shells contain a 1.5-meter-wide capture net. When fired at close range, five tethered segments rotate and extend to create the net, which moves toward the target drone and envelops it, causing it to fall to the ground. The shells are fired from a modified shotgun with a “choke tube” at the muzzle that allows the shell to rotate and extend the net correctly.
Lethal ballistic weapons use standard or custom-designed ammunition to destroy incoming drones.
C3: Communications, Command, and Control
Combating UAS is not a sensor problem. It is a sensor and command problem. Distributed sensors are useless if the data cannot reach an operator with low latency. Response resources are useless if authorization and coordination fail due to interference or overload.
This is where many civilian programs underestimate the complexity. The threat environment can include interference, radio frequency noise, electromagnetic interference, malware infections, etc. If your architecture assumes ideal communications, it will fail under pressure.
A practical design goal is resilience through redundancy.
- Multiple paths and media where possible.
- Degraded mode operations that remain safe.
- Local autonomy for monitoring and alerting, but with human control of response decisions.
- A fusion layer that doesn’t crash if one type of sensor is lost.
The distribution of available resources
Effective detection depends not only on the capacity of the sensors, but also on how they are positioned and integrated within the defended area. In civilian environments, protection should be based on a layered topology.
An outer layer provides early warning and allows for time to be gained, often using long-range radar or electro-optical sensors when geography permits.
An intermediate layer enhances tracking continuity and confidence in threat classification, while a near-layer closes low-altitude gaps with localized electro-optical and acoustic coverage. Response elements are then aligned with defined security zones and the responsible authority.
This layered approach reflects the reality that the defense of civilian sites is fundamentally a matter of time. Systems designed to provide minutes of warning enable controlled and lawful responses. Systems that only provide seconds of warning tend to force risky last-minute actions.
Inter-agency and inter-actor coordination
Civil authorities often cannot guarantee complete coverage of their assets and infrastructure on their own, especially when drones can be launched in the vicinity or approach from multiple directions. Adequate protection increasingly depends on regional coordination and shared detection networks between civilian institutions and state security forces. This is especially relevant for infrastructure clusters such as industrial parks, ports, airports, and railway hubs.
Distributed detection over a wider area can extend early warning, but only when implemented through regional sensor networks governed by clear rules for data sharing, retention, and escalation, and with accountability within their respective areas of influence.
Developing a credible civilian Counter-UAS program
When evaluating or designing a layered detection system, political leaders, as well as those in the armed forces and security agencies of states, should seek clear answers to the following questions:
How does the system merge tracking data between the sensors?
What level of confidence is assigned and what data supports it?
How are false positives measured and reduced over time?
How long is telemetry retained for trend analysis and incident reconstruction?
Therefore, any civil protection program that aims to be credible must consider at least the following five questions:
1. Layered detection with fusion and auditability
Layered detection must be supported by data fusion and auditability. Effective systems generate a unified path picture with confidence levels, explainable correlations between sensors, and retained telemetry for post-event analysis and review, rather than isolated sensor signals or alerts.
2. A defined decision model
Civilian response to unmanned aerial systems (UAS) requires clearly defined roles, decision thresholds, escalation rules, and authority. Actions cannot, and should not, be improvised at the moment of alert.
This aspect could be the most critical point, considering the current lack of political leadership, where the primary concern of civil officials seems to be focused on polls, political survival, or personal interests, to the detriment of serving the citizens who elected them.
3. Response options adapted to the environment
The appropriate set of response tools depends on whether the site is urban, remote, coastal, industrial, or located within logistics corridors. Effectiveness alone is insufficient if the response creates an unacceptable risk to civil safety, whether to people or property.
4. Resilient communications and training
Systems should operate over redundant communication paths whenever possible, support safe degraded modes, and be reinforced through recurring drills to prevent both deviations from procedures and last-minute improvisations.
5. Continuous adaptation
Threat behavior changes. And it changes fast. Sensor calibration changes a. Weather and other conditions change. Therefore, policies must be re validated over time.
On the other hand, data and trend analysis are essential for an effective strategy, rather than a reactive approach to attacks after they have occurred.
Time to act and prevent greater harm
Defending civilian targets against drones requires more than just one anti-drone tool. It requires layered and managerial capabilities that integrate detection, identification, decision authority, resilient communications, and secure response, and that align with broader air defense and critical infrastructure protection strategies, mass events, etc.
As threats evolve, effective deployments will be those that manage counter-UAS as a living system with measurable performance and clear accountability in the event of an attack.
