The service issued a request for information Wednesday to help the program executive office for intelligence, electronic warfare and sensors and the program manager for Next Generation Command and Control (NGC2) get feedback from industry and identify potential solutions.

The Army wants to mitigate the cognitive burden for commanders and boost their situational awareness.

“As the Army continues to integrate advanced technologies and expand its use of unmanned aerial systems (UAS), rotary-wing, fixed-wing, and emerging platforms, traditional airspace management methods are being challenged by the growing scale, speed, and complexity of operations,” officials wrote in the RFI.

“Traditional airspace management systems often struggle to process and respond to the vast amounts of data generated during operations, limiting their ability to provide actionable insights in real time,” they added.

The proliferation of drones will make airspace management even more complicated. The Army and the other services are under pressure from Secretary of Defense Pete Hegseth to quickly integrate more small unmanned aerial systems across the force. Hegseth issued a directive earlier this month with the aim of accelerating that process.

Meanwhile, the Pentagon is also pursuing new counter-drone tools, air-and-missile defense systems, and command-and-control tech to address growing threats.

The expanding use of UAS, loitering munitions and autonomous platforms will have to be taken into account by the U.S. military’s airspace management frameworks, which must also be able to deal with the presence of large numbers of friendly, neutral and enemy players — as well as other weapon systems and adversaries’ electronic warfare capabilities, the RFI noted.

“Army airspace management must adapt to rapidly changing mission requirements, including the need for real-time deconfliction, airspace prioritization, and coordination with joint and coalition forces,” officials wrote. “Effective airspace management must account for the coordination of indirect fires, air defense systems, and other effects to ensure mission success while minimizing risk to friendly forces.”

The Army is hoping artificial intelligence tools can lend a helping hand.

“AI-enabled airspace management solutions have the potential to address these challenges by leveraging machine learning, predictive analytics, and automation to enhance situational awareness, optimize airspace allocation, and enable rapid decision-making. Such systems can analyze real-time data from multiple sources, predict airspace usage patterns, and recommend proactive measures to improve safety, efficiency, and mission effectiveness,” per the RFI.

Responses to the RFI are due Aug. 29.

The service is looking to put vendors’ technologies through their paces later this year at a Joint Pacific Multinational Readiness Center event.

“The Army is seeking interested industry partners to deliver a minimum viable product (MVP) for an AI-enabled airspace management solution that enhances UAS operations during JPMRC Exercise 26-01,” officials wrote. “The MVP must be operationally ready for deployment to the 25th Infantry Division by November 2025 and capable of addressing some of the unique challenges of UAS management in contested and congested environments.”

The post Army wants AI tech to help manage airspace operations appeared first on DefenseScoop.

The sea service’s maritime operations centers, or MOCs, are part of the Navy’s approach to fleet-level command and control and are expected to be “the center” of how sailors fight in a distributed manner in future battles, according to the CNO Navigation Plan released last year.

“MOCs and the processes they execute, whether in one location or disaggregated, are how fleets convert data into information to deliver decision advantage for the commander. MOCs must be capable of integrating with the Joint Force, Allies, and partners to link our fleet commanders to the range of sensors, shooters, and effectors distributed across the battlespace. To integrate a maneuvering, distributed, information-centric fight requires that we treat MOCs as the weapons systems they are,” then-Chief of Naval Operations Adm. Lisa Franchetti wrote.

She tasked all fleet headquarters, beginning with Pacific Fleet, to have MOCs certified and proficient in command and control, information, intelligence, fires, movement and maneuver, protection, and sustainment functions by 2027.

Franchetti was fired in February by the Trump administration amid a broader removal of senior military leaders at the Pentagon in the early months of President Donald Trump’s second term. Adm. James Kilby has been performing the duties of CNO since then.

Navy leaders have identified AI as a tool that could help commanders and the MOCs.

“One area that can help in that is probably in the area of decision-making, in terms of whether it be AI or some other way of creating an advantage for the commander in terms of that OODA loop that [Pacific Fleet Commander] Adm. [Stephen] Koehler referred to, where we take all this tremendous amounts of data that we have and are able to fuse it quickly into a coherent picture that matches the commander’s timing and tempo and sequencing of events that needs to occur as he or she makes those decisions,” Vice Adm. Michael Vernazza, Naval Information Forces commander, said earlier this year at the WEST conference.

The latest outreach to industry from the Defense Innovation Unit comes via a new solicitation for the Situational Awareness by Intelligent Learning Systems, or SAILS, program.

“U.S. Navy assets generate vast amounts of multi-source tactical data from various platforms, including space-based, shipboard, and airborne assets, as well as unstructured data (intelligence reports, watch logs, etc.) produced by sailors. Currently, Maritime Operations Centers (MOCs) must manage and analyze large volumes of multi-source data generated across the fleet to make critical resource allocation decisions for geographically dispersed fleet and national assets,” DIU officials wrote in a problem statement.

“The Navy seeks commercial AI/ML applications that accelerate the convergence of MOC-destined data inputs (e.g. intelligence reports, satellite-derived data, and existing common operational picture tools, etc.) to improve situational awareness for operators, and optimize existing decision support tools by offering track confidence scoring and real-time recommendations to assist commanders in allocating geographically dispersed resources (e.g. satellites, aircraft, vessels, etc),” they added.

Desired attributes for the technologies include watchfloor workflow automation via connection to third-party software and data platforms through APIs to deliver models developed for MOC use cases; provision of models to generate track confidence scores and threshold-based alerts to end-users; generation of sensor and resource allocation recommendations that take into account communication bandwidth conditions, geographic constraints, sensor reliability, past model performance, watchstander availability and other information to inform MOC commanders of asset availability and readiness; and “natural language-based model tuning that allows MOC end-users to interactively adjust objective functions, factors, and constraints” while ensuring that the model’s decision-making process is “maximally interpretable and/or explainable,” among other characteristics.

Solutions should enable role-based access control and cross-domain data sharing, comply with NIST 800-171 cybersecurity controls, support deployment on government or contractor-provided infrastructure and allow for operations across different classification levels, among other technical attributes, according to the solicitation.

Industry responses to the solicitation are due June 6.

The post DIU helping Navy get new AI capabilities for maritime operations centers appeared first on DefenseScoop.

Speaking during his keynote speech at Space Symposium on Tuesday, Spacecom Commander Gen. Stephen Whiting outlined ongoing initiatives to deter and defeat adversaries. The efforts are framed by the combatant command’s new “elements of victory,” and include moves to operationalize new capabilities, develop new technologies and draft two new strategies — one focused on experimentation and another on AI and machine learning.

“Over the past year at U.S. Space Command, we’ve developed elements of victory: our best military judgement for what we think we need to win in a conflict,” Whiting said. “These five elements of victory are informed by lessons learned in other domains — from the best thinking across our Joint Force, exercises and modeling and simulation — and they tell us what we need for war-winning advantage and how we will win.”

Part of the initiative focuses on getting new capabilities for warfighters across Spacecom’s different mission areas. For example, Whiting said the command is working to operationalize a data-fusion system that can create a single common operating picture for missile warning and missile defense missions.

Announced last year as a pilot program to improve data-fusion capabilities, the effort looked to address Spacecom’s ability to digest and view space domain data from multiple systems on a single screen. Since initiating the program, the command has focused on developing a data integration layer for missile warning and missile defense systems and is now demonstrating the capability, Whiting noted.

“Now we’re moving forward with operationalizing this system and placing it on our [Joint Operations Center] floor,” he said. “In the coming months, we’ll be adding additional missions to that program.”

At the same time, Spacecom continues to support research and development of technologies to enable what it calls “dynamic space operations” — or the ability to quickly and continuously maneuver systems on-orbit in order to address emerging threats in that domain.

While the command has repeatedly stressed the need for more maneuverable satellites, the Space Force has put only small amounts of money into research for the capability — and whether or not that funding will continue in future years remains up in the air. Whiting stressed, however, that development of space maneuver capabilities is imperative for Spacecom, especially given recent advancements in China’s ability to freely move their on-orbit satellites. 

To support development, the command will co-sponsor an effort with SpaceWERX — the Space Force’s technology innovation arm — that focuses on sustained space maneuver, according to Whiting.

“We will soon be identifying 10 proposals for $1.9 million each in funding over a 15-month period of performance,” he said. “This effort will continue to invest in the most promising technology from commercial industry to help us solve the sustained space maneuver challenge, so we can bring this joint function to the space domain.”

Other Spacecom initiatives include the deployment of an additional next-generation mobile radar for space domain awareness in the Indo-Pacific; working with organizations across the Pentagon to field more agile command-and-control capabilities; and meeting new demands for offensive and defensive space control.

Along with additional technologies, Whiting said Spacecom is drafting two new strategies that will help the command better prepare for conflict in space. 

“To ensure we maximize our readiness for day-to-day operations so that we are ready for conflict, we are operationalizing the command’s first-ever experimentation strategy and artificial intelligence and machine learning strategy,” Whiting said. He added that the priorities for these strategies focus on space fires, operational space command and control, missile defeat effects, enhanced battlespace awareness, cyber defenses and the command’s business processes.

The post Space Command moves to support new capabilities, strategies for warfare in space appeared first on DefenseScoop.

The contractor announced Monday that it completed flight testing of the Cognitive Algorithm Deployment System (CADS), which employs AI and machine learning technology to an aircraft’s RWR to enable real-time analysis of enemy radar signals. The conclusion of testing paves the way for the Air Force to begin buying the systems in 2025 for some of the service’s fourth-generation aircraft, according to the vendor.

The company designed CADS specifically for its ALR-69A radar warning receiver, Michael Baladjanian, Raytheon’s vice president of electronic warfare systems in advanced products and solutions, told reporters during a briefing ahead of the announcement. At the moment, the company expects CADS to be added to F-16 Fighting Falcons for the Air National Guard and the EC-130H Compass Call, but the contractor is able to integrate the system with any aircraft using the ALR-69A, Baladjanian noted.

“Our ALR-69A, which we’re employing this on, is really the first digital radar warning receiver. It gives the air crew one of the most highly reliably precision data-making systems compared to the old analog legacy systems,” he said. “One of the things when we started this that we looked into was, how would we employ our CADS hardware? And the RWR was a really nice fit for that.”

The CADS incorporates an embedded graphics processing unit and a computing stack developed by Deepwave Digital, a company specializing in developing AI for radio frequency and wireless systems. Baladjanian noted the platform is also able to host third-party software, meaning algorithms developed in the future could be integrated for upgrades down the line. 

The technology uses algorithms to help air crews sense, identify and prioritize enemy radar signals on the RWR’s mission data file in real time, according to Baladjanian.

“The RWR is all dependent on the mission data file, and this actually enhances that characteristic for the radar warning receiver. The CADS works together or in parallel with what’s there today to help [warfighters] identify and even prioritize threats,” he said. “A lot of times you might get flooded with 100 different looks, and it’s going to help the air crew say, “This signal is more important than that one.’”

The AI and machine learning capabilities in CADS can also conduct more accurate data analysis on unknown signals, giving warfighters more confidence in what specific enemy capabilities they are facing, Baladjanian told reporters.

“This will be able to break through a lot of barriers with certain threats, especially when you’re in flight tests. Lab tests are a good step, but there’s nothing like when you go onto an open range and now you’re seeing all kinds of different types of signals,” he said. “This will help break through those ambiguities that can happen.”

Raytheon has so far conducted six lab tests and five flight tests for CADS, and flight tests are expected to continue throughout 2025 as the contractor waits for a procurement decision. While Baladjanian could not share how many systems the Air Force is planning to buy, he said Raytheon has the ability to meet the service’s demand.

The post Raytheon ready to add AI-enabled radar warning receiver to fighter jets, other Air Force platforms appeared first on DefenseScoop.

The effort was part of the Cyber Quest event held in July at Fort Eisenhower, Georgia. Cyber Quest is an experimentation venue where the Army seeks to test emerging technologies on either existing or desired capabilities brought by contractors that respond to specific problem statements from the service in order to help inform future requirements and concepts.

Among the 19 technologies across a broad range of stakeholders tested at this year’s gathering, officials sought to apply algorithms to process and identify so-called signals of interest at the tactical edge.

More so than previous conflicts, such tools will be imperative in future fights where sophisticated adversaries will possess advanced electromagnetic spectrum capabilities designed to locate U.S. forces and jam their communications. In order to thwart these systems, the American military must have countermeasures against them.

However, if soldiers encounter a signal not previously labeled or in their library, countermeasures can’t be developed. During the Cold War, it could take weeks to months between the time a signal of interest was detected, sent back to a lab, analyzed, countermeasure developed and countermeasure delivered to the field.

On the modern battlefield, that likely won’t be sufficient to win.

“That’s key as we move further forward to both reducing the manpower that we need at the edge, but also getting somewhere close to that machine speed processing that is an end goal,” Maj. James Harryman, Cyber Quest lead exercise planner and a U.K. exchange officer at the U.S. Army’s Cyber Center of Excellence, said in a July interview.

Software company DataShapes AI participated at Cyber Quest, putting their algorithms and sensors to the test.

“We’re generating unique waveforms and seeing what kind of categorization it gives us. We’re seeing a lot of success out there and the maturity of some of these ML algorithms,” Col. Gary Brock, capability manager for electronic warfare, who is responsible for developing requirements for new EW systems, said in an interview.

Logan Selby, president and CEO of DataShapes AI, said the company deployed its software to edge devices that could be scattered throughout the battlefield and do real-time detection and classification of signals.

“There’s a signal here in the spectrum — and then [we need to figure out] what is it? We were able to detect them in real time at those edge devices and then classify them on the edge devices as well … We can actually learn signals in real time on the edge devices as well,” he said, noting they demonstrated this at Cyber Quest.

Selby said they’re seeking to enhance kill chains and increase the speed of decision-making for commanders.

“With the speed of battle as it is today and the stuff constantly changing, signals constantly changing the environment, us empowering that soldier — whether they’re a classically trained electronic warfare specialist, or they’re a fueler or a truck driver or somebody that is not classically trained — our tool is extremely intuitive, where it’s allowing them to learn these signals in real time,” he said.

The experimentation at Cyber Quest and desire to test these types of classification tools fits in line with the Army’s larger electronic warfare data pilot to determine what the service needs to be able to rapidly reprogram systems on the battlefield.

Brock said this experimentation ties into the pilot and will soon lead into a Maneuver Fires Integration Experiment at Fort Sill, Oklahoma.

He also noted that as users learn more about AI and ML capabilities related to signal classification, that knowledge will filter back to product managers overseeing ongoing development of EW materiel and requests for information from industry under programs of record.

“Unique to that is as we look at the data structures required to train those algorithms on the fly, we’re feeding that over to the [Army Cyber Command] data pilot team so that we’re working on those,” Brock said.

The post Army tests algorithms for classifying new signals for first time at annual experiment appeared first on DefenseScoop.

The Air Force plans to spend approximately $99 million on the multiyear innovation effort and multiple awards are anticipated, according to the announcement.

The department is seeking research to “design, develop, test, evaluate, and deliver innovative technologies and techniques for Next Generation Target Tracking architectures, which exploit a wide array of data sources and leverage the power of Artificial Intelligence (AI), Machine Learning (ML), and Machine Inferencing (MI) algorithms in a High Performance Computing (HPC) enabled framework,” per the presolicitation.

That includes 3D pixel, vector, and point cloud processing and accelerations, as well as methods to use AI and machine learning for “identification, classification and pattern learning that inference over information from multiple data modalities” such as open-source intelligence, signals intelligence, imagery and geospatial intelligence.

The Air Force Research Lab, which will oversee the effort, also seeks tools to aid the ingestion and processing of GPS, non-GPS, inertial navigation system, radio frequency identification trackers, or telematic-based data into “traffic tracks that can measure utilization of lines of communication,” according to the announcement.

Additionally, the lab is interested in capabilities that can process cellphone GPS and non-GPS data — such as inertial navigation systems, accelerometers, altimeters and personal fitness devices. The technology could help first responders locate vulnerable individuals in disaster areas, Air Force officials say.

Successes prototyping efforts that are funded through other transaction agreements could result in awards for follow-on production contracts, the presolicitation noted.

Vendors seeking funding for fiscal 2025 are advised to submit their white papers by Nov. 30.

Next-generation target tracking is a top modernization priority for the Air Force. For example, a command, control, communications and battle management (C3BM) system for moving target indication is one of two programs that the department recently initiated through a Quick Start rapid acquisition authority granted by Congress in the fiscal 2024 National Defense Authorization Act. It’s aiming to field the first increment in 2027.

Although the service is trying to deploy next-generation target tracking capabilities faster, acquisition chief Andrew Hunter told lawmakers that fully building out a new networking architecture to support those types of tools will take some time.

“If you start talking about really being able to do entire mission threads at scale, anywhere in the world, it’s going to be another few years before we can really say we’ve rolled that out to the warfighter,” he said at a Senate Armed Services Committee hearing in May.

The post Air Force issues presolicitation for next-gen target tracking appeared first on DefenseScoop.

Drone attacks have played a prominent role during the Ukraine-Russia war and the ongoing conflicts in the Red Sea and the Middle East. U.S. legislators have taken notice.

Earlier this week, the House Armed Services Subcommittee on Tactical Air and Land Forces released its mark for the fiscal 2025 Servicemember Quality of Life Improvement and National Defense Authorization Act.

“Unmanned Aerial Systems (UAS) continue to evolve rapidly and present growing threats to the United States and allied personnel and infrastructure. Although many adversarial UAS are inexpensive and easy to replace, U.S. forces often respond with defensive capabilities that are much more expensive, limited in quantity, and slow to replace. Moreover, many legacy systems struggle to effectively counter larger UAS. The committee believes the most effective counter-UAS capabilities for the joint force are those using software-defined technologies of autonomy, artificial intelligence (AI), and machine learning to outpace the current and evolving UAS threats,” the text of the legislation states.

Industry has been developing new tools, including cutting-edge software and weapon systems, that could boost the department’s arsenal of defensive weapons.

Lawmakers praised Central Command, which oversees U.S. military operations in the Middle East, and Special Operations Command for using open-architecture systems and “AI-driven autonomous air vehicles” to take out so-called Group 3 drones — a category of UAS that includes loitering munitions, also known as kamikaze drones or one-way attack drones.

However, legislators dinged the Pentagon for not moving faster to ramp-up production and fielding of innovative technologies to get after these types of air defense problems.

Members of the House Armed Services Committee are “concerned that the military services have not budgeted to sustain and expand these types of critical capabilities in fiscal year 2025 or in the Future Years Defense Program. For example, the Army has failed to transition these capabilities at scale, and the Navy and Air Force lack clear program office direction to begin such transition,” according to the text from the Subcommittee on Tactical Air and Land Forces’ mark for the annual policy bill.

The legislation would direct the secretary of the Army, in coordination with the leaders of the Navy and Air Force, to brief the HASC by mid-December “on plans to resource, transition, and scale advanced, AI-enabled, combat-validated UAS defeat capabilities to conventional forces within the department.”

The provision reflects broader concerns on the Hill about the U.S. military’s posture for defeating unmanned aerial systems.

For example, the mark from the Subcommittee on Tactical Air and Land Forces would also mandate the secretary of defense secretary to name an “executive agent” who would provide oversight of the Pentagon’s training and technology programs to thwart small UAS.

The post Lawmakers plan to press DOD to accelerate fielding of AI-enabled counter-drone capabilities appeared first on DefenseScoop.

A few years, during DARPA’s AlphaDogFight Trials, algorithms went undefeated in computer simulated battles against a military aviator. More recently, the agency’s Air Combat Evolution program has been using a modified F-16 known as the X-62A VISTA (Variable In-flight Simulator Test Aircraft) to put machine learning agents through their paces in the skies above Edwards Air Force Base, California.

A total of 21 test flights were conducted for the project between December 2022 and September 2023, the Department of Defense said in an ACE program update released Wednesday.

“Beginning in December of 2022, that was the first application of machine learning agents to control the flight path of fighter aircraft,” Col. James Valpiana, commandant of the Air Force Test Pilot School, said in a video accompanying the update.

More than 100,000 lines of flight-critical software changes were made over time to improve the tools.

Then, in September, “we actually took the X-62 and flew it against a live manned F-16. We built up in safety using the maneuvers — first defensive, then offensive, then high aspect nose-to-nose engagements where we got as close as 2,000 feet at 1,200 miles per hour,” Lt. Col. Maryann Karlen, deputy commandant of the test pilot school, said in the video.

The exercise marked “the first AI vs human within-visual-range engagement (a.k.a. ‘dogfight’), conducted with actual manned F-16 aircraft,” the DOD said in the program update.

At publication, the department had not provided information to DefenseScoop about whether the machine learning agents controlling the X-62A beat the human pilot in that encounter.

However, defense officials touted the importance of these efforts for demonstrating that artificial intelligence technologies can operate safely in a complex warfighting environments such as close-in, air-to-air combat.

“In advance of formal verification methods for AI-based autonomy, the team pioneered new methods to train and test AI agent compliance with safety requirements, including flight envelope protection and aerial/ground collision avoidance, as well as with ethical requirements including combat training rules, weapons engagement zones, and clear avenues of fire,” according to the DOD program update.

“The X-62A team demonstrated that cutting-edge machine learning based autonomy could be safely used to fly dynamic combat maneuvers. The team accomplished this while complying with American norms for safe and ethical use of autonomous technology,” Secretary of the Air Force Frank Kendall said in a video, adding that the capability is “transformational.”

Officials noted that human pilots were onboard the autonomous aircraft in case anything went awry during the test flights and they needed to take over, but personnel did not have to activate the safety switch during the dogfights over Edwards Air Force Base.

Other organizations supporting the program include Calspan, Cubic Corporation, EpiSci, Lockheed Martin Skunk Works, physicsAI, Shield AI, the University of Iowa Operator Performance Laboratory, Johns Hopkins Applied Physics Laboratory, the MIT Computer Science and Artificial Intelligence Laboratory, and the MIT Lincoln Laboratory.

Kendall, a strong proponent of U.S. military adoption of AI, told lawmakers last week that he intends to fly aboard an F-16 in autonomous flight mode later this year.

Kendall has said the successes of the ACE program contributed to his decision to move ahead with the Air Force’s collaborative combat aircraft (CCA) program, an effort to develop and field next-generation autonomous drones for counter-air operations and other missions. The service plans to spend billions of dollars on that initiative in the coming years.

“The critical problem on the battlefield is time. And AI will be able to do much more complicated things much more accurately and much faster than human beings can. If a human being is in the loop, you will lose. You can have human supervision, you can watch over what the AI is doing, [but] if you try to intervene, you’re going to lose,” Kendall said during a panel at the Reagan National Defense Forum in December.

“I just got briefed by DARPA on some work that they’re doing on manned versus unmanned combat — basically aircraft fighters,” Kendall said. “The AI wins routinely with the way they structured the test … but the difference in how long it takes the person to do something and how long it takes the AI to do something is the key difference in the outcome. And we’re talking about seconds here. Just to give you a sense of parameters here, the best pilot you’re ever going to find is going to take a few tenths of a second to do something. The AI is going to do it in a microsecond — it’s gonna be orders of magnitude better performance. And those times actually matter. And you can’t get around that. But that’s the reality that we’re gonna have to face.”

The post Pentagon takes AI dogfighting to next level in real-world flight tests against human F-16 pilot appeared first on DefenseScoop.

The service’s integrated program executive office for command, control, communications and battle management (C3BM) issued a sources-sought notice Monday on Sam.gov for an “artificial intelligence and machine learning toolkit” that could improve reaction times.

Specifically, the service wants to apply the so-called toolkit to its cloud-based command and control (CBC2) effort. The Air Force is casting a broad net for capabilities that could be included in the toolkit, underscoring that AI and ML technologies can be used for different applications and problems, according to the request for information.

“This effort shall be a collection of tools and technologies that improve tactical C2 software applications under development within multiple programs (e.g., Cloud-Based Command and Control) and reduce operational workflow timelines for C2,” the RFI stated.

CBC2 is a key component of the Air Force’s Advanced Battle Management System initiative and the Pentagon’s Joint All-Domain Command and Control (JADC2) effort. The warfighting concept aims to connect sensors and shooters from across the U.S. military and international partners under a single network, enabling faster and more effective decision-making and employment of forces.

The Air Force delivered an initial operating capability of CBC2 to the North American Aerospace Defense Command’s Eastern and Canadian air defense sector in October 2023. The service plans to continue scaling that capability to other air defense sectors throughout this year.

The platform integrates hundreds of critical air defense radar and data feeds under one cloud-based interface, then develops courses of action from which leaders can quickly make high-quality decisions. Artificial intelligence and machine learning are used to assist commanders in the decision-making process and help maintain situational awareness

Now, the RFI indicates that the Air Force is interested in incorporating other advanced and commercialized AI and ML technologies — including data collection and curation; machine-to-machine operations; large language models; and continuous and reinforced learning training models.

A full statement of objectives was not publicly available on Sam.gov because some of the information related to the notice was “controlled” access.

Responses to the RFI are due April 26.

The post Air Force looks to industry to provide AI ‘toolkit’ for cloud-based C2 capability appeared first on DefenseScoop.

As the U.S. military is preparing for potential battles with sophisticated nation-states, the way in which wars will be carried out is expected to be much different than past conflicts. Units will be dispersed across vast distances and the forces that act the fastest will be successful.

“We will be operating disaggregated and dispersed in the next fight. One hundred percent true. The speed and that fight will be the difference in whether we win or lose. Whether our nation is going to win or lose the next war will be about how quick we can action targets. We got to close this kill chains fast,” Andrew Evans, director of the intelligence, surveillance and reconnaissance task force, said during a presentation at the Global Force Symposium in Huntsville, Alabama, Wednesday. “To do that we have to understand the ecosystem in which we’re operating and we have to find ways to connect distributed sensors quickly at the classification level that a warfighter needs — not an intel professional maybe, but a warfighter.”

Evans noted how traditionally, the concept of sensing has been intelligence focused, meaning the military and intelligence community built exquisite systems and collection mechanisms that mostly served the IC, which operates at higher classification levels and different authorities than average warfighters.

In the future, this intel must be readily available to troops at the tactical edge to be able to act upon conditions to thwart adversary activities.

“If you’re a warfighter you would say, ‘I’m sure we have great things, I just don’t get it. Right. I don’t get it at my level.’ As we talked about what it means to be ready to fight a war in the next fight, we got to move past that. Sensing for multi-domain operations, I think, is going to look different,” Evans said.

The Army, through the new cross-functional team — which was announced this week — and the deputy chief of staff for intelligence will have to hold the IC accountable to ensure tactical requirements are being addressed.

“One example is if you leave it up to the way it might exist today, an IC partner might deliver data down to a reasonable place on the ground that might not be good enough for the Army. The Army needs that data to be moved to the last tactical mile, if you will,” Evans told reporters. “This CFT in conjunction with the G2 will engage the IC often to ensure that tactical needs of the Army are being addressed, because this is again about warfighting … But we’ve got to keep the focus on tactical warfighting needs.”

He said the Army is already seeing a demand for so-called sensor-to-shooter activity tapping into data below the top secret level, and in some cases, the unclassified level.

“How do we make that shift to more traditional reconnaissance, surveillance and target acquisition?” he asked.

One way to do that at speed is thinking differently about what a sensor is, including what Evans called non-traditional sensors.

“Most of our major combat systems today either are, or will be, built on digital backbones, which means they are running on thousands of micro services and micro sensors. We got to find a way to connect all that because those are sensors across the battlefield,” he said. “Those are not your big, exquisite, expensive intel sensors of old. These are sensors that help us optimize combat systems. Why can’t they help us optimize the way we sense in distributed ways?”

A perfect example is radar warning receivers on helicopters that help pilots survive by looking for enemy radar. At its core, these receivers are signals intelligence systems, Evans said, adding that the Army will begin to connect those types of capabilities to a larger architecture to make that data more available and actionable to forces in the fight.

The first line of effort for the new CFT calls for multi-sensor “dominance,” which deals with prioritizing, integrating and shaping sensor technologies.

“We have sensors or things that can sense all over the battlefield today. We have capabilities, we have all kinds of different things that sense,” Michael Monteleone, director of the cross-functional team, told reporters — noting that journalists’ phones, not traditionally thought of as sensors, are now sensors being used to record his remarks. “We may not be taking as much advantage as we could of all the sensing capabilities that we have out there today. Which means not only just hey, we can capitalize on that, but also in some cases we don’t always have to build something either. Sometimes having three of something of less fidelity is just as good as having one of very high fidelity and very high cost.”

Another major shift the Army is gearing up for is in the processing, exploitation and dissemination of intelligence information, or PED.

With more and more sensors being delivered to the battlefield, PED will become more daunting. However, the answer to the problem should not be more people, but rather, artificial intelligence and machine learning to help make sense of what is and isn’t important, officials have suggested.

“You know what a lot of are really, really smart analysts would do? They’d spent an entire shift reporting, ‘Sir, there was nothing significant to report.’ An entire shift. We cannot do that in the future,” Evans said. “We have to take our humans and we have to apply judgment and discretion to whatever the machine presents to them for evaluation. A machine should tell you there was nothing important to look at, but these few things you really need to go take a look at. Then the human should look at those few things. That’s how you keep up with data in the next fight.”

Evans said when building new capabilities, the Army should start by asking what AI or ML needs to be developed to optimize them, which will ultimately better optimize the human enterprise.

“When we combine the data literacy training that will be needed for our humans with this, what I’ll call an intel version of human-machine integration, we will generate exponential enhancements in the speed of decision-making and that will allow us to match the tempo of a data-driven” operation, he said.

The Army will also need to develop stronger edge computing capabilities to enable this type of intelligence and PED to process back and forth from static, remote hubs to soldiers on the ground.

“Edge compute has got to be a piece of that. We think that bandwidth will be limited and contested, certainly,” Evans said. “We have to figure out how [to] operate in those environments. And that’s going to involve doing as much of your compute forward as you can, as much of your processing of the sensor as you can and bringing back the relevant data.”

The post New Army sensing CFT to focus on getting intelligence to tactical edge appeared first on DefenseScoop.