The New Front Line: How Autonomous C-UAS Is Transforming Battlefield Defense

The war in Ukraine has become a testing ground showcasing the evolving capability of drones, as well as the increasing role of technologies such as artificial intelligence (AI) and machine learning (ML). Drones are becoming faster, more sophisticated and able to carry larger payloads, with AI playing an increasing role.

The Ukraine conflict offers a vision of what the battlefield of the future might look like in a conflict against a near-peer or near-peer-supported adversary. Drones are not only an increasing threat, but are evolving rapidly with the adoption of new technologies. It’s vital that leaders begin to plan now to deal with not just the drone threat that we see today, but the evolving threat of tomorrow.

Tactics are evolving rapidly in Ukraine, with Ukrainian forces recently pioneering a combined-arms assault using drones and ground robots. Other developments, such as AI-driven threats and the use of drone swarms and asymmetric threats, mean that traditional defenses are no longer enough. With traditional methods struggling to mitigate the threats emerging from the battlefields of Ukraine, it’s clear that a new approach is needed, and a more holistic approach to sensor technology, combined with the use of AI will be a major part of this. Autonomous counter-unmanned aerial systems (C-UAS) will play a vital role in protecting forces going forward and will be a key tool to maintain operational security much like the Active Protection Systems (APS) that are already in service for the larger armored vehicle with the soft-kill and hard-kill measures.

In Ukraine, drones are being used for multiple roles, in everything from surveillance to kamikaze attacks, and it has never been more urgent to detect and deal with such threats as efficiently as possible. With drone technology evolving so rapidly, a single-sensor approach is no longer adequate. To deal with new and ever-changing drone threats, a multiple sensor approach including sensors such as acoustic, radar and RF sensors will be vital to build truly comprehensive defense networks, along with thermal detection and optical targeting. Simple one-way drone attacks are growing and are increasing in volume: to ensure warfighters can deal with such attacks, it’s vital that they are equipped with systems with autonomy, armed with optical precision targeting with AI and ML target recognition and autonomous slew-to-cue.

Writing in the Military Review in 2024, then Major General Joel B Vowell and Major Anthony R Padalino said, ‘Existing mission command systems fielded to counter enemy UAS lack necessary technological capabilities to adequately defend combat power on today’s battlefield. Mission command systems for counter-UAS (C-UAS) require artificial intelligence (AI), machine learning, and automation to assist operator decision-making and enable simultaneous employment of defeat mechanisms.’

In light of this continuing evolution of drone technology and how drones are used on the battlefield, technologies that can work autonomously to identify, engage and destroy targets are growing in importance. In previous decades, autonomy had become something of a taboo in discussions around military technology, but when it comes to the threat of drones which are cheaply produced, packed with explosives, and launched from locations hundreds of miles away, systems need to be able to detect at long range, and automatically target when within weapon range. In this context, autonomy is vital to the safety of the warfighter. Early detection is vital for human countermeasures. The less early detection available, the more the war fighter needs autonomous sensors, so the only reaction needed is to arm and engage. Leave the processing to the computers, so humans can focus on the authorization to execute.

AI and ML algorithms will also come into their own here, helping to rapidly identify and categorize drones. C-UAS systems need to be able to rapidly detect drones, track them, calculate firing solutions and engage, independent of human intervention. In the current evolving threat landscape, C-UAS is growing rapidly in importance, and it’s clear that networked equipment, autonomous systems and AI will be vital when it comes to keeping warfighters safe.

Fully integrated C-UAS systems will enable operators to disrupt, degrade and delay enemy attacks. This offers a vital advantage, with computer vision and AI helping to increase situational awareness and offering early warnings of approaching drones. This is why a multi-sensor approach is so key in delivering for warfighters. By taking a multi-sensor approach to C-UAS, targets can first be detected by radar, then at closer range by optical, then dealt with as they move into weapons range. By handling this autonomously, C-UAS systems can be faster than human response at the tactical level with the appropriate sensors.

It’s clear that not only is the use of one-way unmanned aerial system attacks growing in conflicts such as Ukraine, but that this trend will continue. Cheaply produced one-way drone attacks, packed with explosives, are a challenge to existing mission command systems. Lacking capability and interoperability, traditional mission command systems are ill-equipped to deal with the evolving smaller threats we see on the battlefields of Ukraine.

Autonomous C-UAS, with a multi-sensor approach and built on AI algorithms, can deliver a cost-effective way to extend awareness, stay ahead of evolving threats and keep warfighters safe.

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