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In the event of a conflict or confrontation, the joint and allied force could lose access to satellite capabilities, most notably GPS. Ships, submarines, and aircraft would need to rely almost entirely on other technologies for positioning, navigation and timing (PNT), particularly inertial systems.

Unfortunately, because inertial navigation devices such as gyroscopes and accelerometers lose accuracy over time—and wouldn’t be able to be recalibrated in a GPS-denied environment—inertial navigation would be reliable for only a limited period.

Comprehensive operation plans (OPLANs) can help integrate the U.S. and its allies and partners across the Indo-Pacific—but to stay ahead of fast-moving changes in the region, it is increasingly important that the plans be frequently and rapidly updated. The challenge is that OPLANs tend to be static documents that often must be updated manually, a process that can be cumbersome, time-consuming, and incomplete.

An excerpt from an interview between Bill Hamblet, Editor-In-Chief of Proceedings, and Scott Spence, the Executive Director of Naval Integrated Solutions at Raytheon Missiles and Defense.

One of the challenges in integrating the U.S. and its allies and partners in the Indo-Pacific is that there is a great deal of complexity in how a potential adversary might engage each of the different countries in different ways leading up to a conflict——tactically, strategically, economically, and politically. And there is just as much complexity in how each country might respond in its own way.

In the coming years, the joint forces will increasingly use artificial intelligence in unmanned systems in the Pacific. Many of the algorithms will be mission-specific and classified, making them potential targets of adversaries who may try to steal or disrupt them.

Protecting classified algorithms in unmanned systems in the Pacific presents a unique set of challenges. Unmanned systems may operate closer to adversaries than manned systems. And with unmanned systems, humans may not be available to detect attacks on the AI and take corrective measures.

One of the thorniest challenges in the Indo-Pacific is persistent targeting—how can the joint forces keep track of a constantly changing array of often fast-moving targets, over vast open spaces, against adversaries adept at hiding what they’re doing? How can you make sure you’re always matching up the right sensors with the right targets, and at exactly the right times, so you can maintain custody on critical targets with the needed handoff from
one sensor to the next?

Back in the 1990s, when the U.S. military still relied primarily on line-of-sight rather than satellites for C4ISR and other communications, the Office of Naval Research developed and tested a breakthrough approach—a self-organizing mesh network for Navy line-of-sight communications.

Unmanned maritime systems (UMS) are poised to become a leading-edge capability for the Navy in potentially contested environments in the Western Pacific. As this unfolds, China will likely respond by aggressively introducing new methods and solutions to blunt the UMS’ effectiveness. The Navy will then need to introduce even more advanced sensors, analytics and other technologies – which the Chinese in turn will seek to counter as quickly as they can.

Unmanned maritime systems (UMS) are poised to become a leading-edge capability for the Navy in potentially contested environments in the Western Pacific. As this unfolds, China will likely respond by aggressively introducing new methods and solutions to blunt the UMS’ effectiveness. The Navy will then need to introduce even more advanced sensors, analytics and other technologies – which the Chinese in turn will seek to counter as quickly as they can.

The inherent challenges associated with managing cloud systems in disrupted, disconnected, intermittent and low-bandwidth (DDIL) environments requires looking at the issue from multiple perspectives.

The U.S. Navy has a 30-year shipbuilding plan. But distributed maritime operations is built around C2, ISR and logistics. The enablers are becoming more than that; they are becoming key weapons systems themselves.

A credible Pacific deterrence posture for the U.S. Navy requires that the fleet of ships, submarines, and aircraft be available to the combatant commander at a rate that outpaces potential adversaries, in order to maintain control of strategic geographic areas and vital supply chains. A new, AI-enabled approach to predictive maintenance can help achieve this goal, and increase operational availability across the INDOPACOM AOR and elsewhere.

As large-scale cyberattacks by China and Russia on American government agencies and corporations have demonstrated, it can be difficult to prevent nation-states from planting malware on sensitive networks—even those with strict access controls. It can also be difficult to know that it has happened. Suspected Russian hackers in the SolarWinds supply-chain attack remained undetected on networks for as long as nine months before they were discovered.

In the coming years the Navy will gain access to a rapidly growing profusion of sensors, not just through new fleets of unmanned vehicles combined with existing systems, but through

multi-service sensors as well, as part of a joint operating environment. If the Navy is to maintain dominance in the INDOPACOM AOR, it must be able to extract maximum insight from those sensor assets.

In the not-too-distant future, large unmanned Navy vehicles—both surface and undersea—may be regularly patrolling the waters of the South China Sea, equipped with sophisticated sensors, formidable weapon systems, and advanced analytics. As with any emerging military technology—particularly those with new, untested missions—much about how this will play out can’t be fully predicted.