As drone airframes become commodities, the value lives in the system that thinks

Drone airframes are now commodities. Capabilities that were unusual five years ago are available from a growing number of manufacturers, and performance in areas such as speed, endurance, and payload is improving on a hardware cycle measured increasingly in months rather than years.

That trend is visible both in the drones used as threats and in the systems designed to defeat them. As airframe performance converges, the basis of differentiation is shifting rapidly. At Fortem, we believe more of the value in the counter-drone space will reside in what the system as a whole can sense, interpret, and do autonomously.

We’ve come to that conclusion through the evolution of TrueView®, the radar technology on which Fortem is built. From the outset, over a decade ago, TrueView was small in size, lightweight, power-efficient, and low-cost – what is known in military and aerospace procurement circles as a low-SWaP-C sensor. In subsequent discussions with our defense customers, it became clear that those characteristics could make the radar particularly useful for counter-UAS missions, where detecting and tracking small drones was becoming an increasingly urgent challenge. Encouraged by a defense contract, we put TrueView onboard an interceptor, which ultimately led us to design and build DroneHunter® – a drone with six rotors that can capture rogue or hostile drones in mid-air using a net.

Placing a TrueView R20 radar directly onboard DroneHunter meant the aircraft was not entirely dependent on a ground sensor and a communication link to complete an engagement. It could generate its own local view of the airspace, search for and lock onto a target, hold track, and compute its own intercept trajectory – adjusting in real time through evasive maneuvers. That alone changed what an interceptor could do without continuous ground oversight.

But detection and tracking are only part of the problem. An operational system must also tell drones from everything else in the sky with enough reliability to support a decision to engage. In Fortem’s TrueView R40 radar, that classification runs at the edge, inside the radar itself. Machine-learning models separate drones from non-drones, and flag what they can’t resolve as unknown rather than guessing. The models improve with each software update. With the target classified and tracked, Fortem’s SkyDome® Manager command-and-control software can pass the task of threat defeat to DroneHunter.

SkyDome remains essential, as does the human role in setting mission parameters and engagement rules, but tracking and navigation no longer need to depend entirely on information generated elsewhere. Humans determine the rules of engagement; autonomous systems execute them with speed, accuracy, and effectiveness that no operator can match. Human on the loop, not in it.

That distinction matters most against swarms, where the traditional model – detect a threat, evaluate it, assign an interceptor, manage the engagement, then repeat – places a practical limit on how many targets operators can handle simultaneously. Greater onboard autonomy is the difference between a system that scales and one that doesn’t.

This has implications for how counter-UAS systems should be evaluated. Speed, range, endurance, payload, and unit cost remain important, but they do not fully capture how a system performs when communications are degraded, the number of targets rises sharply, and human speed and error are accounted for. Procurement decisions should consider how much of the target picture an interceptor can generate for itself, how dependent it is on continuous external communications, how reliably its sensors can distinguish targets from clutter, and how many simultaneous engagements a single operator can effectively supervise.

These factors shape the true cost of a counter-UAS architecture. An inexpensive interceptor may not produce an inexpensive system if each additional interceptor in flight requires a corresponding increase in operators, communications infrastructure, or sophisticated and expensive ground cueing systems. A system that needs one operator per drone, or a defeat mechanism that must continuously paint its target, does not scale well. With capable airframes widely available, the harder and potentially more durable source of differentiation is increasingly the sensing, software, and autonomy built around them. In other words, the system that thinks.