(Washington, D.C.) Fighter-jet-mounted guns, air-dropped bombs, ship-launched interceptor missiles, and ground-fired air defenses can only be as effective as the accuracy and timeliness of their targeting intelligence, a simple, known reality becoming increasingly pressing and relevant as the Pentagon surges ahead with its multi-domain networking strategy. As part of this networking strategy, the Army is accelerating development of its Integrated Air and Missile Defense Battle Command System (IBCS).
Integrated Air and Missile Defense Battle Command System (IBCS)
The system consists of a collection of radars, fire control systems, weapons, and targeting nodes connected to one another to seamlessly share time-sensitive data in real-time. IBCS, which recently shot down several cruise missile targets in a live-fire test, is slated to become operational next year.
If an F-35 fighter jet is in position to attack an enemy ship first located by ground-based radar or ship-integrated targeting sensors, then targeting details need to be identified and transmitted in real-time.
For this to happen, large volumes of intelligence, surveillance, and reconnaissance (ISR) nodes need to organize, process, and analyze incoming sensor data to identify moments, objects or developments of significance to human decision makers.
Artificial Intelligence (AI)
The growing amount of ISR data is a main reason why the Defense Department and its military services are moving so quickly to implement artificial intelligence (AI), as it could arguably provide a backbone to a massive joint network.
“Industry can deliver seekers, warheads, propellants, and radar systems, yet there is a point at which you get cognitive overload at the soldier level. We can use AI to reach out and query a sensor to determine what we want to bring in,” Army Maj. Gen. Robert Rasch, the program executive officer of Missiles and Space, told an audience at the Space and Missile Defense Symposium in Huntsville, Alabama.
Rasch went on to explain that analyzing, organizing and sharing sensor data through AI and Machine Learning “aids filtering,” as the machine can understand what sensors find and bring it into our network.”
