Following recent drone incidents over European airspace, the counter-drone response has converged on layered sensor architectures.

This consensus for layered sensor architectures builds on a simple logic. Every sensor has blind spots, whereas a layered network ensures that what one sensor misses, another catches.

For instance, a drone flying at low altitude behind vegetation may operate below radar detection thresholds, remain obscured from optical surveillance, and emit no RF signature during autonomous navigation. In this scenario, integrating acoustic sensors in the layered architecture would enable detection of the drone's sound waves.

The Case for Layers

While appealing to deploy a single sensor type, airspace intrusions rarely entail ideal conditions for detection.

Visuals degrade at night, in rain, and at range. Radar is effective against larger targets but can struggle with small, slow-moving drones at low altitude. RF detection depends on the drone actively transmitting, which autonomous and pre-programmed platforms do not. Acoustic sensors are passive and continuous, but their effectiveness is reduced in some environments. However, none of these limitations are failures of technology. They are inherent properties of how each sensor interacts with the physical world.

A layered architecture addresses this by distributing the detection and classification burden across multiple modalities. When sensors are integrated into a shared command-and-control framework, their output can be correlated in real time. A detection from one sensor can cue another and a classification from one modality can be confirmed or challenged by another.

The operational objective, is thus not detection alone but to verify, classify, and provide  actionable assessments and documentation.  

Cameras & Acoustics

To understand how sensor layering works in practice, it may help to examine a pairing between cameras and acoustic sensors. EO/IR systems are widely deployed tools in drone detection. They provide visual or thermal imagery that operators can interpret directly. The challenge is, however, that they require direction.

This is where acoustic sensors provide a complementary capability. A distributed array of acoustic sensors can monitor a wide area continuously, detecting and localising airborne sources based on the sound they emit. When a drone is detected acoustically, a and its position triangulated, this position can be passed to a camera system as a cue. Given this information, the camera may now be directed toward a specific area of sky, enabling the camera to perform a visual or thermal verification.

Lessons from NATO SET-348

This value of layered sensor integration was recently demonstrated in the collaboration between BSS and Circle Scope during NATO STO SET-348.

Here, BSS deployed its passive acoustic system, Komodo, which provided wide-area UAS detection, 3D localisation, and classification using advanced and embedded signal processing. In parallel, Circle Scope partook with their AI-enhanced EO/IR cameras for target verification, utilising BSS’ acoustic detection data to autonomously acquire and maintain focus on designated targets.

Operationally, Komodo’s detections generated 3D localisations that cued Circle Scope's cameras to acquire and lock on the target. This enabled fast handoff from early warning and classification to confirmed visual identification.

The lessons from NATO SET-348 are relevant beyond the exercise environment.

Airports, military installations, ports, energy infrastructure, and other sensitive locations all face the same threats from above. Consequently, the future of C-UAS may very well depend on sensor fusion as drones become harder to detect as well as distinguish from other airborne objects.

In essence, authorities, security agencies, and armed forces will need layered sensor architectures that can provide reliable data regardless of the operational conditions.