Japan studies AI-powered anti-ship missiles that think together during flight.

Japan’s Defense Ministry plans to develop a new generation of anti-ship missiles, equipped with AI-powered warheads, that will communicate mid-flight with decoys and jammers to coordinate attacks against enemy ships.

As reported by the Yomiuri Shimbun on November 11, 2025, Japan’s Defense Ministry has outlined a program to test cooperative artificial intelligence for a new generation of anti-ship missiles to improve coordination and strike efficiency at long range. The system under study would allow missiles to communicate during flight, adjust to target maneuvers, and maintain engagement despite interference or jamming. An initial budget of about 200 million yen for fiscal 2026 will fund research into secure communication, contested-spectrum resilience, and mission safety protocols.
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Japan’s Defense Ministry plans to develop a new generation of anti-ship missiles, equipped with AI-powered warheads, that will communicate mid-flight with decoys and jammers to coordinate attacks against enemy ships. (Picture source: Army Recognition)

The Japanese Defense Ministry has proposed a control system that would allow multiple missiles, potentially including missiles with AI-assisted seekers and warheads, to communicate in flight and adapt to changing conditions, with an initial budget line scheduled to begin next fiscal year and a multi-year assessment targeting earliest practical employment around fiscal 2029 under AI risk management and mission safety guidelines. The program emphasizes keeping human officers responsible for mission intent and engagement authorization while evaluating higher-performance AI and the technical, operational, and legal trade-offs before any decision to field such systems. The concept aims to move beyond single-shot guidance models toward cooperating salvos that share sensor data, update tracks, and re-plan trajectories when targets maneuver or defenses react.

This approach pairs strike missiles with AI-enabled jammers and decoy missiles that can coordinate to complicate an adversary’s engagement calculus and increase the chance of successful penetration, particularly as standoff ranges approach and exceed 1,000 kilometers, where longer flight times raise exposure to interference and interception. Under current concepts, each missile would retain its own seeker for terminal identification, while a cooperative layer would share contact updates, platform health, and dynamic role changes so that the loss or jamming of one component does not automatically collapse the entire engagement. The ministry has framed the work as strengthening the Self-Defense Forces’ ability to counter distant vessels and to raise the cost of aggression by increasing defender uncertainty, and it has stated that experiments will be bounded by explicit human-in-the-loop controls and safety constraints throughout testing.

At the program level, the Japanese ministry has proposed roughly 200 million yen in the fiscal 2026 request to evaluate higher-performance AI over a three-year period and to assess cost-effectiveness and technical risk before any production commitment. The planned assessment will study contested-spectrum resilience so that a cooperating salvo can revert to pre-planned profiles if communications are denied, and it will examine encryption, authentication, and survivable datalinks to prevent hostile takeover or spoofing of the cooperative network. The technical architecture under study emphasizes flexible in-flight updates, coordinated deception with decoys, and mission safety controls that align with established rules for AI use on defense equipment. The timeline and budget posture reflect both the technical complexity of adding autonomy to lethal systems and the policy need to assure safe, predictable behavior; human officers are to retain authority to approve mission profiles and engagement parameters during all phases of testing and any subsequent deployment decision.

Partial precedents for linked or semi-autonomous anti-ship engagement behavior exist in contemporary and legacy systems, even if none publicly match the full cooperative AI concept under study by the ministry. The U.S. AGM-158C LRASM combines low observable design, autonomous target acquisition, and a datalink that enables coordinated attack behavior among multiple missiles, allowing them to share targeting context and deconflict approach angles in flight. Israel’s Sea Breaker family uses AI-assisted scene-matching and automatic target recognition to operate in GNSS-denied littoral environments and to select aim points without continuous external guidance. Soviet and Russian design schools explored coordinated salvo tactics decades ago, with systems such as the P-700 Granit described as employing leader-follower behavior in group launches where one missile classifies and assigns targets while others execute attack profiles, although the operational depth and peer-to-peer networking in service use varied by design and doctrine.

Earlier Soviet-era examples and modern export offerings illustrate intermediate steps toward fully cooperative salvos and onboard AI, without implying that every element of the Japanese concept is already fielded. The P-120 Malakhit and similar legacy missiles incorporated mid-course update provisions that were not true peer-to-peer cooperation but foreshadowed the logic of connected engagement and offboard retasking. Many contemporary export-grade anti-ship cruise missiles feature two-way datalinks that provide re-targeting, battle-damage feedback, and seeker update paths, which can serve as the backbone for coordinated salvo behavior when combined with onboard processing. Publicly confirmed instances of an explicitly AI-equipped warhead remain limited; most disclosed autonomy today resides in guidance, target discrimination, and terminal acquisition rather than in adaptive fuzing or effect selection within the payload, and any future warhead-level adaptation would raise additional technical, legal, and doctrinal questions.

The tactical advantages of cooperation and onboard AI for anti-ship salvos are concrete and drive the operational rationale for the ministry’s work. Cooperative salvos can assign complementary roles during flight, synchronize time-on-target from multiple bearings, and stagger altitudes and approach vectors to stretch radar coverage and interceptor allocation, thereby raising the probability that at least part of an incoming wave penetrates. Shared sensing and classification among missiles helps the group reject decoys, reduce fratricide risks in congested sea lanes, and maintain continuous target custody during evasive maneuvers, which lowers the chance that a single lost link or jammed seeker causes mission failure. The coordinated use of decoys and AI-enabled jammers forces defenders to expend interceptors and to engage ambiguous tracks, multiplying the effect of each launched round and increasing defender uncertainty about which track is the real threat.

There are also inventory and deterrence implications that follow from smarter salvos and integrated electronic attack. Smarter salvo behavior can reduce the number of rounds needed to achieve a given effect by minimizing redundant aim points and by prioritizing disabling strikes against sensors, propulsion, or command nodes instead of distributing impacts randomly. Coordinated deception and electronic attack increase the defender’s cost per engagement by inducing wasted intercepts and by diluting effective threat filters across multiple ambiguous contacts. At theater ranges, these capabilities raise the perceived cost and risk of aggression, complicate adversary shot doctrine and allocation, and can change how opposing commanders value assets and routes, thereby contributing to deterrence by increasing uncertainty and expected interceptor expenditure even before weapons are launched.

Architectures to implement cooperation span leader-follower models and fully distributed meshes, and both approaches require distinct tradeoffs in robustness, processing demand, and network complexity. In a leader-follower model, a designated missile might briefly extend its sensor horizon to correlate contacts and then transmit target and route updates to low-flying peers over encrypted links, which simplifies decision authority but creates a potential single point of failure. In a distributed mesh, each missile functions as a node that shares tracklets, emitter detections, and health status so the cluster can vote on target identity, assign impact sectors, and coordinate terminal timing without reliance on one leader, at the cost of higher onboard processing and more complex network management. Two-way datalinks in either model permit in-flight retasking from offboard sensors and post-impact damage assessment that can inform follow-on strikes, while robust fallback modes are required so salvos can execute pre-planned profiles if communications are denied or interference exceeds resilience thresholds.

At the algorithmic and operational control level, onboard AI can support layered autonomy while maintaining human authority over mission-level choices and engagement rules. During cruise phases, learning-based classifiers can fuse passive RF, imaging infrared, and radar cues to maintain track quality in cluttered seas and to tolerate degraded signals or spoofing attempts, and near the terminal phase, AI-assisted discrimination can help reject corner reflectors, towed decoys, or false emitters and select aim points that maximize system-level effects against a confirmed target class. The concept of adaptive payload logic that would adjust fuzing delays or attack geometry based on observed structural signatures exists in theory but is not publicly confirmed as an operational capability, and any deployment of such functionality would require additional safety, legal, and operational safeguards. System designs under study thus include guarded autonomy with enforced no-strike constraints, hardened communications and authentication to prevent hostile manipulation, and explicit human override and clear operational authorities at every stage of engagement.

Japan’s program ties these technical concepts to explicit policy and testing steps intended to balance capability gains with safety and legal constraints while retaining human control. The initial funding tranche and three-year evaluation period will examine contested-spectrum resilience, encryption and authentication, survivable datalinks, and the cost-effectiveness of higher-performance AI before any production decision. The ministry’s stated plan foresees earliest practical employment around fiscal 2029 if tests validate effectiveness and safety, and the planning documents emphasize human officers retaining authority to approve mission profiles and engagement parameters during all phases. The program, therefore, represents a deliberate shift toward coordinated, adaptive salvos that fuse sensing, autonomy, and electronic attack while embedding mission safety controls, fallback modes, and legal safeguards as part of the development and assessment process.

Written by Jérôme Brahy

Jérôme Brahy is a defense analyst and documentalist at Army Recognition. He specializes in naval modernization, aviation, drones, armored vehicles, and artillery, with a focus on strategic developments in the United States, China, Ukraine, Russia, Türkiye, and Belgium. His analyses go beyond the facts, providing context, identifying key actors, and explaining why defense news matters on a global scale.