Three Imperatives in Integrated Air and Missile Defense Sensemaking

Somewhere in North Korea, a long-range hypersonic boost-glide missile destined for the American mainland is launched. An ICBM-class booster carries the weapon into space before its front-end glider separates, re-enters the atmosphere, and maneuvers toward its target. Space-based infrared sensors detect the missile’s exhaust plume, providing an initial indication to elements of the U.S. missile defense system (MDS). As the plume fades, AI-enabled systems classify the missile, calculate its trajectory, and estimate its potential target. Unlike ballistic missile warheads, hypersonic glide vehicles can maneuver—making their trajectories and intended targets difficult to determine. AI can continuously update those calculations as information moves among space-based assets, ground- and sea-based sensors, and command and control centers—reducing latency and helping the MDS optimize its defense. During the missile’s initial boost and ascent phase—potentially lasting only one to three minutes—commanders must determine how and when to respond before the missile transitions to its atmospheric re-entry phase.

Do they have enough time?

The rising scale of data collection and the need to extract insights from this data in Integrated Air and Missile Defense (IAMD) are renewing the need for sensemaking: the ability to rapidly interpret incoming information, identify threats, and select courses of action without delay. In IAMD, effective sensemaking must help operators process more data, determine a threat’s likely target and trajectory, and select the appropriate intercept response—all within compressed timelines.

The United States’ adversaries are heightening this burden by developing systems with greater range and speed, as well as uncrewed drone swarms. Legacy IAMD must do more with incoming data with less time to make sense of it.

To keep pace, the U.S. MDS requires greater sensemaking capacity across three imperatives:

(1)   Maintaining situational awareness amid the real-time collection of open- and/or closed-source data.

(2)   Reducing the cognitive burden on commanders and operators as they oversee increasingly complex human-machine teams that process relevant data in the critical period of missile interception.

(3)   Reducing the time required to make decisions at all echelons of the MDS.

On all three fronts, sensemaking is essential to turning data into decision advantage.

AI-enabled Sensemaking

Given the proliferation of threats and associated data, some forms of AI are promising candidates for improved sensemaking. AI models, albeit with varying strengths depending on the domain (like sensors), are adept at rapidly detecting patterns in large quantities of data. To retain sensemaking capacity in a battlespace defined by the speed, distance to target, and likely trajectory of the threat, a fine-tuned capability would discriminate among data and produce courses of action for the human commander.

Such a capability is conceivable across all layers of the MDS.

Consider the space layer: Problems for commanders arise upon the detection of a missile launch. Space-Based Interceptors (SBIs) will autonomously – and with a speed that exceeds the human ability to maintain situational awareness - carry out certain tasks for shot selection, including tracking the missile and calculating the optimal use of interceptors to disable it. The result is a rapidly expanding stream of data requiring immediate interpretation.

By processing sensor data directly in space, onboard AI can detect threats earlier, separate critical signals from background noise, and reduce the amount of data that must be sent to ground systems for human commanders. This cuts latency, delivers actionable information faster, and enables commanders to make quicker, more informed decisions, ultimately improving mission effectiveness while reducing overall system costs.

IAMD is increasingly designed with system-of-systems thinking in which space-based assets are merely one layer. This in turn requires new ways of orchestrating across domains and accessing information that does make its way to human personnel.

How will sensemaking be secured for human commanders?

AI-enabled software can carve this problem at its joints: this software can ingest incoming data from multiple sources or modalities, fuse it, and produce interpretable courses of action for human selection. These systems can be fine-tuned for mission-specific purposes within the broader MDS, presenting those courses of action most relevant to the human commander’s responsibilities, reducing the time between threat identification and action without sacrificing sensemaking.

IAMD today requires a new paradigm for sensemaking in an increasingly data-saturated battlespace. AI-enabled capabilities drive this transformation across three innovation imperatives: networked situational awareness, human-machine decision support, and accelerated decision velocity.