Think of a missile seeker as the missile’s ‘eyes and brain’ that guides it to a target. This report analyzes how AI is upgrading those eyes and brain so missiles can recognize targets more accurately, adapt to changing conditions in flight, and ignore decoys—similar to how modern cars use AI to recognize lanes and obstacles, but in a far more demanding military environment.
Traditional missile seekers struggle with modern battlefields: stealthy or fast-moving targets, heavy jamming and deception, cluttered environments (urban, maritime, bad weather), and the need for rapid, precise engagement with minimal collateral damage. AI-enabled seekers promise better target detection, classification, tracking, and resilience to countermeasures, while reducing operator workload and improving mission success probability.
Defense primes and sensor OEMs can build moats via proprietary threat libraries, classified training data from test ranges and combat operations, tightly integrated hardware–software architectures for real-time AI on edge compute, and long certification cycles that favor established vendors over new entrants.
Hybrid
Unknown
High (Custom Models/Infra)
Onboard compute and power constraints for running AI models in real time on ruggedized, thermally constrained missile hardware; plus data scarcity and secrecy for training robust models across diverse threat environments.
Early Adopters
Compared to generic AI-in-defense discussions, this use case focuses narrowly on the seeker subsystem of missiles—where AI models must run at the edge under extreme constraints, fuse multiple sensor modalities (e.g., IR, RF, optical), and survive rigorous military certification. Vendors that can prove reliable performance in contested electromagnetic environments and provide upgradeable AI software stacks for existing missile families will stand out.