Current decades-old platforms cannot adequately leverage new technology; and the supporting structures to enable future C2 either do not exist or require maturation. DoD officials have stressed that a JADC2 architecture would enable commanders to 1) rapidly understand the battlespace, 2) direct forces faster than adversaries, and 3) deliver phase linked combat effects across all domains.
JADC2 envisions providing a network battlespace for the Joint force to share intelligence, surveillance, and reconnaissance data, transmitting across many communications networks, to enable faster decision making.
JADC2 intends to enable commanders to make better decisions by collecting data from numerous sensors, processing the data using artificial intelligence algorithms to identify targets, then recommending the optimal weapon—both kinetic and nonkinetic e.g. electronic weapons to engage the target.
DoD uses commercial ride-sharing services as an example to describe its desired end state for JADC2. Ride-share services combine two different apps—one for riders and a second for drivers. Using the respective users’ position, the AI tool determines the optimal match based on distance, travel time, and passengers, among other variables.
The ride-share application seamlessly provides directions for the driver to follow, delivering the passenger to their destination. DoD has its own network requirements to transmit data to match riders and provide driving instructions.
JADC2 is not an easy endeavor. Sharing information across multiple security layers, harvesting data and then turning it into accessible, discoverable and transportable information is a challenge.
Army Network Exercise Tested Sharing of Targeting Data from Newest Weapons” Army is looking at the potential integration of all of our fires into a fires network. Currently, Army has one network, AFATDS, to pass data about ground targets to its offensive artillery units – howitzers, rocket launchers, surface-to-surface missiles.
Meanwhile, it’s developing a different network, IBCS, to share data on flying targets – incoming enemy rockets, missiles, and aircraft – with its air and missile defense units.
The two networks and the sensors that feed them must meet very different technical demands, since shooting down a missile requires split-second precision that bombarding a tank battalion does not.
But there’s also great potential for the two to share data and work together. For example, the defensive side can figure out where enemy missiles are launching from, then tell the offensive side so it can blow up the enemy launchers before they fire again.
While this year’s Convergence exercise focus on the Army, the service is already working with the Air Force to meld the two. “We have been in discussion with the Air Force for the better part of the year on how we integrate with the effort they have going on like live meetings on JADC2 [Joint All-Domain Command & Control] with all of the architects of ABMS.”
Those discussions made very clear to both the Army and the Air Force participants that “it all comes down to data and it all comes down to the architectures you build.
A common theme emerges from Army efforts to field several new communications technologies: for the foreseeable future, the service's comms programs are about pushing more data to and from the front lines in the face of increasingly aggressive electronic-warfare activities.
Two Army pilot programs aim to bring cloud storage closer to the front lines.
The goal of the first pilot, which operators have just finished testing, was to move training software from a fixed location into a cloud for use anywhere. That will be useful as the Army deploys its new Integrated Visual Augmentation System, or IVAS: augmented-reality goggles that will allow soldiers to review and retrain on different operations they’ve experienced.
The second pilot looks at virtual and container clouds — basically, smaller cloud environments within larger clouds. The objective is to sideline data that operators use only rarely, and prioritize access to more valuable data in environments where there is a lot of jamming and hacking.
The Army has built several prototype communication tools highlighting new prototypes in cryptography alternatives, as in methods for sending secure coms beyond traditional encryption, for tracking friendly military units (also in electronic warfare-heavy environments) and satellite communications tools that rely less on commercial, wideband satellite signals.
They’re also working with the service’s Future Vertical Lift team — manned and unmanned helicopters — to build wideband satellite communications gear that can “operate on our platforms through rotor blades.”
The Army is working with industry on prototypes for multi-orbit and multi-path (meaning in low-earth orbit, geo earth orbit, etc.) satellite communication tools, software-defined radio programs for the CMOSS standard, which refers to the modular open suite of software standards that allows for different military vehicles to share the same software platform, unified network operations, identity management, data transfer that’s less hackable or jammable, and techniques for converging disparate data sources into a common fabric.
Technologies have come out of the Combat Capabilities Development Command ready for wider, experimental use in the field include cyber situational understanding, application security, integrated tactical network operations, canceling interference for the TSM waveform, which is commonly used in tactical settings, and greater spectrum awareness “so we can see what our signature looks like and take actions to mitigate against that signature.”
Communications are a modernization priority since it provides the basis for sharing data between a wider array of Army robotic vehicles and device-carrying soldiers. It remains to be seen how well all of those Army pieces will connect to other services as part of the Pentagon’s new Joint All-Domain Command and Control networks.
To find targets for its new long-range weapons, the Army is experimenting with cloud computing and AI that can bridge the gap between intelligence networks and combat units.
It’s critical to get data on potential targets from intelligence systems to combat units fast enough to strike them with the new long-range artillery and high-speed helicopters now in development. Digitally connecting the widest possible range of “sensors” to “shooters” in this way is the focus of Army Futures Command’s Project Convergence experiment.
The types of data you want to access range from full-motion video to electronic warfare detections of enemy transmitters. Quickly pooling that many kinds of data, from that many different sources, will require heavy use of artificial intelligence and cloud computing.
“We’ve initiated two kinds of cloud pilots to help inform how we’re going to adopt cloud within the tactical environment, but that’s very different from implementing cloud in the benign environment of an Army base in the US.
When people say data is “in the cloud,” that means it’s on someone else’s computer, usually a massive server farm somewhere, that you can access remotely. No access, no cloud, no data. But frontline units can’t drag fiber optic cable behind them wherever they go. Tactical networks have to move data over radio transmissions, which can be disrupted by terrain – mountains, reinforced concrete buildings, even dense forest – or by enemy hacking and jamming.
Future exercises will require moving larger amounts of data over longer distances than ever before. “Our integrated tactical network is really being pushed to the limit so extending its range and capacity are high priorities.
“We’re going to be extremely challenged by the bandwidth of the network. That means you can’t dump all the data on a single, central mega-server and expect everyone to access it over long-distance links. Instead, you need to selectively decentralize the data, pushing at least some crucial subset of it all the way out to the frontline edges of your network.
“Data that we don’t access routinely, we can host inside the cloud. “Data we need to access more rapidly and on demand, we can put into edge nodes.”
“We have to spend some time to really understand how we want to deliver data and to whom, when, and where. “One of the things that we’ve been working on is expanding our Army commercial cloud provider services to edge nodes provided by the same vendor… How do you get that data in and out of a FOB [Forward Operating Base]? How do you get that on a plane? How do you move it around the world?”
Army is not trying to do all things in the cloud instead trying to figure out what are the things we can containerize right away.
Sorting all this data and moving it through the cloud requires automation. Today, moving data from intelligence collectors to combat operators is “a very labor-intensive process.
In the future it needs to start “doing that work in a much more digital way that’s a lot more seamless and easier to do at scale and at a distance.”
One potential solution: artificial intelligence. Army artillery units did a live-fire experiment with targeting data provided by two complementary pieces of AI software, Prometheus and SHOT (Synchronized High-Optempo Targeting).
“Prometheus is looking at all that intelligence imagery and being able to pluck out potential targets of interest, and doing it very successfully.” Prometheus’s output then feeds into SHOT “where we’re looking at how do we automate and use machine learning … to come up with the right information to address the attack guidance matrix so we can actually do effective calls for fire.”
Prometheus analyses satellite data, spots enemy forces, and sends targeting data to shooters. The artillery corps is working on a complementary AI called SHOT (Synchronized High-Optempo Targeting), which will take targeting data from Prometheus and other sources, match it against the commander’s priorities and an Attack Guidance Matrix stating which types of targets are best attacked by which types of weapons. Then SHOT will output an automated call-for-fire, something missing from this year’s exercises.
AI is still young and “brittle,” Its machine-learning algorithms need to be trained on much more data before they can reliably detect a wide range of targets in a wide range of environments, not just the open desert.
Experiments in what’s called Joint All Domain Command & Control, like the Army’s Project Convergence and the Air Force’s ABMS “on-ramps,” aim to automate that process by transmitting data directly from machine to machine. The vision is that spy-satellite photos and other reconnaissance information flow into artificial intelligence software, which spots concealed foes far faster than human analysts can, picks priority targets and sends precise targeting data to whatever friendly unit is best armed and located to strike, be it an aircraft, ground-based missile launcher, or other weapon.
“Can we actually look at the intelligence community to help feed us tactically and provide us tactical data for deep fire targeting with long position fires?
“And then the last piece of this, which we will address probably on the follow-on phase is BDA, how do we do battle damage assessment?” “How do we use machine learning to determine that my effects actually were effective?”
There’s no one master algorithm that can solve all of these different problems. “There’s no one-size-fits-all machine learning capability out there,” We’re on the verge right now of a big wave of machine learning capabilities coming out of industry…. Can we actually put multiple machine learning methods into the architectures?”
But no amount of new technology will be enough without the human element. Success will also take policy, procedural, and even cultural changes to streamline coordination between intelligence networks.
“Policy and data governance, at scale …. whether it be at the enterprise level, or the at the tactical level-- that is a gap that continues to challenge us at least every day
Some critics take a skeptical approach to JADC2. They raise questions about its technical maturity and affordability, and whether it is even possible to field a network that can securely and reliably connect sensors to shooters and support command and control in a lethal, electronic warfare-rich battlespace.
Critics ask who would have decision making authority across domains because traditionally, command authorities are delegated in each domain rather than from an overall campaign perspective.
- What is the relative priority for JADC2 compared with other major DoD programs?
- How have combatant commands embraced the JADC2 concept?
- Is there some resistance within DoD?
- What are the initial takeaways from Army and Air Force demos to implement JADC2?
- How do Navy/Marine requirements differ from the other services to implement new command and control concept?
- What personnel, equipment, facilities, and training resources would be required to achieve JADC2?
- What is the estimated cost for force-wide implementation and lifecycle upkeep of JADC2?
- When could the network become operational?
- What role would AI have in JADC2 development?
- How much human-in-the-loop decision making is necessary if sensors are linked to shooters in real-time?