A wreckage of a recently intercepted Shahed-136 MS001 drone revealed an advanced, autonomous, and AI-driven system capable of operating in GPS-denied environments. Countering this technology, BSS’ passive acoustic C-UAS solution provides covert and cost-effective situational awareness.

In June 2025, the Ukrainian Ministry of Defence published the contents of a recently intercepted Shahed-136 MS001 drone to an online database (source). While resembling its predecessors, the wreckage revealed an advanced system architecture diverging from familiar loitering munitions.

Previous Shahed-type drones have relied on preloaded trajectories and centralised commands. However, the wrecked Shahed-136 MS001 included a high-speed processor, infrared imaging capabilities, and telemetry systems.

In other words, contemporary adversarial drones are transitioning away from being remotely triggered to autonomous and algorithmically driven threats.

A closed-loop system

At the centre of the downed Shahed’s system was a compact AI processor designed for edge-based computing, machine vision, and low-latency inference. Integrated with an infrared sensor array, the unit enables real-time image recognition and object identification. In practice, this allows the drone to dynamically adjust its course, identify objects, and prioritise targets.

Furthermore, the wreckage revealed the presence of a radio modem, indicating swarm logic, which enables these drone types to attack cooperatively. The system’s navigation had been reinforced by a Nasir GNSS module, enhanced with a CRPA antenna system and an eight-channel reception providing spoof resistance. Together, these elements form a closed-loop decision system without human latency.

Conclusively, these findings illuminate a drone variant capable of navigating autonomously and bypassing degraded GPS environments.

1000 daily drone attacks

As of early 2024, the front line has shifted its centre of gravity. Instead, drones now target energy grids, logistics hubs, and civilian infrastructure, seeking to exhaust operational cohesion. In relation to this, Shahed-type drones have been tailored to fulfil the role as highly effective loitering munitions due to their low-cost and high scalability.

Furthermore, on July 4, a Ukrainian commander warned that Russian drone strikes could escalate to 1000 per day (source). At this unprecedented rate, drone swarms will soon saturate radar coverage and deplete ammunition depots.

The scale of deployment is consequently no longer hypothetical. With streamlined production lines and modular upgrades based on off-the-shelf components, the cost-performance equation of drone warfare has been fundamentally redefined.

Passive Acoustic C-UAS, a silent counter

Within this evolving threat environment, passive acoustic C-UAS sensors provide a silent advantage. Unlike radar or active RF systems, these sensors detect sound patterns and are unaffected by jamming, unspoofable, and inherently covert. BSS’ Komodo exemplifies this approach.

Rendered invisible to adversaries, the low-cost C-UAS sensor is capable of distinguishing between acoustic profiles in real time and uses embedded signal processing for accurate detection at ranges up to 5 km. The Komodo can thereby detect type 1 and 2 drones such as the Shahed, Orlan, and quadcopters through advanced machine learning and convolutional neural networks (CNNs).

The Komodo's lightweight construction supports both fixed and mobile deployment, while its low power requirements and minimal training overhead allow for high scalability. Furthermore, the Komodo can be integrated as part of a layered sensor architecture to expand the detection envelope and provide threat confirmation in contested environments.

When signal saturation and electronic denial impairs active sensors, passive acoustic C-UAS solutions provide an efficient alternative.

And in this context, the Komodo listens when others cannot see.