Just before dawn, an adversary launches a massive amphibious assault on a U.S. ally. The adversary is well aware that the Americans have thousands of unmanned aerial, surface and undersea drones in the area, bent on creating a “hellscape” to slow the attack until the calvary arrives.
But the adversary doesn’t believe that the unmanned vehicles will be a deciding factor. While it expects to sustain some losses, the adversary is convinced that the UVs’ counterattack will be too scattershot, too unorganized, to seriously hamper the assault. In addition, the adversary is already jamming and otherwise disabling U.S. satellites overhead, disrupting communications.
However, the adversary has badly miscalculated. Far from being unorganized, the combined fleets of undersea, surface and aerial UVs are working closely together, deciding among themselves who should go after which targets to bring maximum lethality.
This is essentially unmanned joint fires, and it goes far beyond making sure systems from different manufacturers can talk with one another. The unmanned vehicles intercepting the attack are actually part of what might be thought of a “swarm intelligence.” The UVs are not just communicating, they’re collaborating—as a unified, deadly force, based on priorities and parameters set by commanders. And the UV fleets are able to do all this without relying on satellites, through a “mesh” communications network at the tactical edge.
Defense organizations may soon have the ability to take advantage of this advanced approach, which brings together a number of existing capabilities—such as modeling and simulation and AI—in new ways.
A key step in this approach is the modeling out of millions of possible scenarios, which are then used by the UVs to coordinate their counterattack. Armed with those scenarios, the UV fleets already know—well before the battle begins—the best ways to counter any number of adversary COAs. They already know which UVs will best be able to pick off an adversary’s ship, submarine, or aircraft, based on each UV’s relative location, speed, payload and a host of other factors. And once the fighting starts, the UVs—with the help of AI—can adapt that knowledge as the constantly changing, real-life scenario plays out.
SETTING THE STAGE
Much of the pre-battle work takes place in a digital environment. One of the first steps is gathering digital twins—virtual representations—of the various joint force UVs, showing their capabilities, including their strengths and weaknesses. This information is fed into numerous scenarios of how the adversary might launch an amphibious assault on our ally.
Through the many simulations, the joint forces can learn how well different UVs are likely to perform in specific situations. For example, based on information from the digital twins, one UUV with loitering munitions might have a better chance than other UUVs in reaching a particular target without getting killed. Or, if the adversary executes a certain COA, here’s the best lineup of aerial, surface and undersea drones, with their associated payloads, to deliver the most lethality.
Defense organizations currently have a model for this digital world—the Joint Simulation Environment (JSE), developed by the Navy and Air Force, in which pilots and others can train in highly realistic virtual scenarios. While the JSE is primarily used for training, defense organizations can adapt its approaches to create a comparable simulation environment for unmanned joint fires.
BRINGING IN AI
As the combined UV fleets work together to disrupt the adversary’s assault, they can take advantage of another emerging technology—the mesh network. This enables the UVs to create their own internet at the tactical edge. Then with AI, the UVs are able to share what they see and work together. What emerges is the swarm intelligence.
When possible, satellites are part of this intelligence, contributing to the mesh network and performing a variety of tasks for unmanned joint fires. But even if satellite communications are denied, the mesh network won’t be affected. That’s because the mesh network is self-healing. As satellites and other UVs are lost in the fight, those that remain keep the network going.
In a battle, it’s not enough for the various UVs to talk to one another, or even to see what the others see. They need to collaborate toward common goals. An emerging form of AI, the “AI agent,” can play a key role here. What distinguishes AI agents from most conventional AI is that instead of just providing information, they work to achieve goals.
Long before a conflict breaks out, AI engineers program the agents with specific goals, from the tactical to the strategic. For example, “Find the most efficient way of coordinating aerial, surface and undersea drones to take out the adversary’s roll-on-roll-off vessels while maintaining the greatest follow-on capability.”
The AI agents answer these kinds of questions by looking at the simulations that are run in the digital environment—they see what works and what doesn’t, and why. A counter- attack with unmanned joint fires might use dozens of AI agents, including certain ones that look at the big picture and the larger goals, and ask, “How can we work together— as a single, unified force—to win not just the skirmishes, but the overall fight?” In essence, the AI agents are just doing the math.
UNMANNED JOINT FIRES IN ACTION
Once the battle begins, all the elements of unmanned joint fires come together. And because the AI agents have already learned from the simulations, they don’t need to start from scratch as the fight takes unexpected turns—they can adapt their knowledge to a rapidly changing battle landscape.
Commanders, of course, are not going to turn over the battle to the unmanned vehicles unless they have confidence the UVs are going to kill the right things, and with the maximum lethality and efficiency. What can give them the confidence is seeing how unmanned joint fires has succeeded in the simulations— they know it works because they’ve run it. In addition, the entire process, from the simulations and AI agents to the battle itself, is guided by commanders’ tactical and strategic priorities, as well as their parameters and guardrails.
Lastly, this approach to unmanned joint fires can serve as a powerful deterrent. With it, we know exactly how our UVs can counter the adversary’s every move. And the adversary knows we know.
VICE ADMIRAL ROY KITCHENER, [email protected], a senior executive advisor at Booz Allen, served as Commander, Naval Surface Forces/Naval Surface Force, U.S. Pacific Fleet. During his 39 years of service, his commands included destroyers, a cruiser and an expeditionary strike group.
REAR ADMIRAL MIKE BERNACCHI, [email protected], last served as Deputy Commander, Tenth Fleet and U.S. Navy Space Command. A 36-year Navy veteran, he started the first AI division in the U.S. Space Command. He provides expertise to Booz Allen in AI, Space/ Cyberspace, and a variety of other technical areas.