The extension was awarded Nov. 9 to incumbent provider Perspecta, which after a merger holds the contract originally awarded to HP Enterprise Services.

The indefinite delivery/indefinite quantity contract has a maximum value of nearly $486 million dollars, and adds a new option period that will extend the potential ordering period by eight months from Oct. 1, 2019, through May 31, 2020.

In early September, Perspecta was awarded as similar contract worth $787 million that added an option period extending the potential ordering period by 12 months from October 1, 2018, through September 30, 2019. Work on the contract will be performed in the United States, Europe, Guam, Korea and Japan.

Under the NGEN contract, Perspecta operates the Navy Marine Corps Intranet and provides IT and network security services for the Navy and the Marine Corps. The Navy is preparing for a broad effort to modernize and improve IT services, and has been working toward issuing Next Generation Enterprise Network Re-compete (NGEN-R) contracts.

The NGEN-R contract is divided into two segments: an end user hardware (EUHW) segment, and a service management, integration and transport (SMIT) segment. The EUHW segment focuses on hardware as a service and hardware for purchase, such as cellphones, laptops and tablets. SMIT prioritizes network services such as system integration, software core build services and computer network defense.

The Navy issued requests for proposals for the EUHW segment on September 18, though RFPs were originally expected over the summer. The deadline for proposals is November 19.

The latest extension to the NGEN contract, designed to give the Navy more flexibility in cloud and IT buying, follows a late September announcement on a separate Navy cloud procurement. The Navy awarded CSRA LLC, a General Dynamics Information Technology company, a blanket purchase agreement for commercial cloud services. The one-year base agreement comes with four additional one-year options which, if exercised, total nearly $96 million.

The classified Gunsmoke-L program is operated by U.S. Army Space and Missile Defense Command/Army Forces Strategic Command (USASMDC/ARSTRAT)Technical Center. The two-year contract, plus an additional option year, to Dynetics is valued at $8.3 million. The contract falls under the umbrella of the Army’s the Design, Development, Demonstration and Integration (D3I), Domain 1 contracting tool, which steamlines the acquisition cycle of space, high-altitude and missile defense technologies.

The Gunsmoke-L satellites will carry next generation tactical space support payloads designed to operation in low earth orbit for a minimum of two years, and could last as long as five years. Dynetics will assist with on-orbit demonstrations using the Technical Center’s ground control system at Redstone to provide command, control, and communication with the satellites. The satellites are expected to be ready for launch in less than two years. Dynetics did not provide any information about the launch of the satellite.

The Army’s Gunsmoke-J satellites, the previous iteration, are expected to be used to “demonstrate advanced information collection in direct support of the Army combat operations,” according to an Army fact sheet from September 2018. The Army discussed its Gunsmoke-G satellite experiment during a classified session of the DoD experiment review board in 2017.

Dynetics will perform hardware-in-the-loop testing and simulation at USASMDC/ARSTRAT Payload Development Laboratory at Redstone Arsenal in Huntsville, Ala., focusing on the 12 kilogram satellites’ performance and reliability. At the end of the period, the two Gunsmoke-L TSSVs will be qualified and ready for launch. Work on the program will predominately be done at Redstone, but some work will also occur at Dynetics own facilities in Huntsville.

“We are looking forward to taking on this challenge of creating small satellites that will meet the Department of Defense’s space support goals,” said Mike Graves, Dynetics space systems department manager and Gunsmoke-L program manager.

Dynetics has previously worked with NASA on other small satellite projects. The company helped develop NASA’s Fast, Affordable, Science and Technology Satellite, or FASTSAT, launched in November 2010.

The goal of the satellite constellation is to “provide survivable, global, secure, protected and jam-resistant communications for high-priority military ground, sea and air assets,” according to an Air Force statement. Maj. Matthew Getts of the Air Force’s Space and Missile Center in Los Angeles said AEHF-4 “will complete the fully operational constellation for AEHF.”

This means the fourth AEHF satellite meets the minimum constellation requirements to bring global Extended Data Rate (XDR) connectivity online. XDR reportedly provides approximately 10 times more communications throughput than the Air Force’s legacy satellite system, known as Milstar. That difference in speed allows the transmission of real-time video, battlefield maps and targeting data to help combatant commanders and national leaders make optimal tactical and strategic decisions.

The prime contractor on the Air Force’s $15 billion program,, Lockheed Martin is under contract to deliver a total of six AEHF satellites. These satellites will be used by the United States and international partners including the United Kingdom, Canada and the Netherlands. The launch of the next satellite, AEHF-5, is scheduled for July 2019, Getts said.

“It’s good to return with our mission partners to see the culmination of expertise, skill and partnership that we have worked diligently toward to make this AEHF launch a success,” said Mike Cacheiro, vice president of protected communication systems at Lockheed Martin. “This is a substantial milestone for AEHF, and as we look ahead, we continue to improve and upgrade this mission to deliver these vital communications capabilities to the Air Force.”

In today’s factories, 3-D printing parts requires persistent monitoring by specialists to ensure intricate parts are produced without impurities and imperfections that can compromise the integrity of the part overall. To improve this laborious process, the Navy is tasking Lockheed Martin with developing multi-axis robots that use lasers to deposit material and oversee the printing of parts.

Lockheed Martin has multiple partners on the contract including Carnegie Mellon University, Iowa State University, Colorado School of Mines, America Makes, GKN and Wolf Robotics and Oak Ridge National Laboratory.

The contract covers what Glynn Adams, a senior engineer with Lockheed Martin, describes as the pre-flight model of the program’s development. Initial work will focus on developing computer models that can predict the microstructures and mechanical properties of 3-D printed materials to generate simulation data to train with. Adams said the Carnegie Mellon team will look at variables such as, “the spot size of the laser beam, the rate of feed of the titanium wire [and]the total amount energy density input into the material while it is being manufactured.” This information helps the team predict the microstructure, or organizational structure of a material on a very small scale, that influences the physical properties of the additive manufactured part.

This data will then be shared with Iowa State, who will plug the information into a model that predicts the mechanical properties of the printed component. By taking temperature and spot size measurements, the team can also ensure they are, “accurately controlling energy density, the power of both the laser and the hot wire that goes into the process,” Adams said..

“All of that is happening before you actually try to do any kind of machine learning or artificial neural networks with the robot itself. That’s just to try to train the models to the point where we have confidence in the models,” Adams said.

Sounds easy, right?

But one key problem could come in cleaning up the data and removing excess noise from the measurements.

“Thermal measurements are pretty easy and not data intensive, but when you start looking at optical measurements you can collect just an enormous amount of data that is difficult to manage,” Adams explained. Lockheed Martin wants to learn how shrink the size of that dataset without sacrificing key parameters. The Colorado School of Mines and America Makes will tackle the problem of compressing and manipulating this data to extract the key information needed to train the algorithms.

After this work has been completed, the algorithms then will be sent to Oak Ridge National Laboratory, where robots will begin producing 3-D titanium parts and learn how to reliably construct geometrically and structurally sound parts. This portion of the program will confront challenges from the additive manufacturing and AI components of the project.

On the additive manufacturing side, the team will work with new manufacturing process, “trying to understand exactly what the primary, secondary and tertiary interactions are between all those different process parameters,” Adams said. “If you think about it, as you are building the part depending on the geometric complexity, now those interactions change based on the path the robot has to take to manufacture that part. One of the biggest challenges is going to be to understand exactly which of those parameters are the primary, which are the tertiary and to what level of control we need to be able to manipulate or control those process parameters in order to generate the confidence in the parts that we want.”

At the same time, researchers also will tackle AI machine learning challenges. Like with other AI programs, it’s crucial the algorithm is learning the right information, the right way. The models will give the algorithms a good starting point, but Adams said this will be an iterative process that depends on the algorithm’s ability to self-correct. “At some point, there are some inaccuracies that could come into that model,” Adams explained. “So now, the system itself has to understand it may be getting into a regime that is not going to produce the mechanical properties or microstructures that you want, and be able to self-correct to make certain that instead of going into that regime it goes into a regime that produces the geometric part that you want.”

With a complete algorithm that can be trusted to produce structurally sound 3-D printed parts, time-consuming post-production inspections will become a thing of the past. Instead of nondestructive inspections and evaluations, if you “have enough control on the process, enough in situ measurements, enough models to show that that process and the robot performed exactly as you thought it would, and produced a part that you know what its capabilities are going to be, you can immediately deploy that part,” said Adams. “That’s the end game, that’s what we’re trying to get to, is to build the quality into the part instead of inspecting it in afterwards.”

Confidence in 3-D printed parts could have dramatic consequences for soldiers are across the services. As opposed to waiting for replacement parts, service members could readily search a database of components, find the part they need and have a replacement they can trust in hours rather than days or weeks. “When you can trust a robotic system to make a quality part, that opens the door to who can build usable parts and where you build them,” said Zach Loftus, Lockheed Martin Fellow for additive manufacturing. “Think about sustainment and how a maintainer can print a replacement part at sea, or a mechanic print a replacement part for a truck deep in the desert. This takes 3-D printing to the next, big step of deployment.”

That’s why the U.S. Marine Corps and NATO allies will be testing these capabilities in Norway later this month during Exercise Trident Juncture 18. More than 40,000 participants, 130 aircraft, 70 naval vessels and 10,000 vehicles are set to participate in the live-fire and computer-assisted command post exercise.

But some of those vehicles will be autonomous weapon systems that are set to demonstrate their ability to reduce the need for dedicated manpower on often dangerous resupply missions.

Describing the expeditionary logistics experiment, NATO Allied Command Transformation said the “aim of this experiment is to demonstrate the capabilities of autonomous weapon systems, in force protection, building and delivery of supplies to isolated troops, through hazard zones.”

Autonomous systems will also be on display during enhanced logistics base experiments. They are expected to demonstrate the ability of autonomous and automated systems with an aim toward significantly improving military logistics by upgrading services and downgrading manpower.

To test the ability of autonomous weapon systems to protect expeditionary bases, NATO forces will “demonstrate a set-up where unattended ground sensors, shot detection sensors and camera-based sensors are fused and report to a unified user interface on the Command and Control system. The activity will incorporate unmanned ground, air and surface systems in the sensor package. Remotely operated weapon stations will be operated both as sensors and as weapon platforms to engage enemy threats,” according to the alliance.

As with other experiments during the exercise, the point of the drill is to demonstrate how autonomous systems can reduce the number of personnel needed for key expeditionary missions.

Some U.S. and NATO adversaries have taken steps beyond testing autonomous units in exercises. Russian Deputy Minister of Defence Yuriy Borisov confirmed via Russian-state media in May that the country had deployed the Uran-9 armed robot tank in Syria.

This contract covers the 11th and 12th satellites in the Air Force’s next-generation GPS III constellation and represent the first space vehicles in the GPS IIIF, or GPS III follow on, program. The Pentagon announcement said the competition included “one solicitation mailed and one solicitation received” but did not offer further details.

The contract requires Lockheed Martin to provide non-recurring engineering, space vehicle test bed and simulators, production of GPS IIIF Space Vehicles 11 and 12, in addition to the production of up to 22 GPS IIIF space vehicles, space vehicle storage, and launch and on-orbit support. These satellites will feature an upgraded payload that will grant the clock signal for a new GPS III Search and Rescue (SAR) payload, and extend the satellite’s service life. The total contract value could reach $7.2 billion.

The first 10 GPS III satellite navigation payload includes “a Mission Data Unit (MDU) with a unique 70 percent digital design that links atomic clocks, radiation-hardened computers and powerful transmitters,” according to a Harris release. This allows for signals three times more accurate than those on current GPS satellites, the company said.

The first GPS III satellite was designated as available for launch in September 2017. It is expected to liftoff aboard a SpaceX Falcon 9 rocket from Cape Canaveral Air Force Station in Florida before the end of 2018. The next satellite will launch aboard a rocket from United Launch Alliance. The U.S. Air Force intends to procure up to 22 new satellites during the second phase of GPS III follow-on, with the first vehicle planned for a 2026 delivery.

To tackle this problem, the Intelligence Advanced Research Projects Activity, or IARPA, conducted the Functional Map of the World (fMoW) TopCoder challenge from July 2017 through February 2018, inviting researchers in industry and academia to develop deep learning algorithms capable of scanning and identifying different classes of objects in satellite imagery. IARPA curated a dataset of 1 million annotated, high-resolution satellite images aggregated using automated algorithms and crowd sourced images for competitors to train their algorithms to classify objects into 63 classes, such as airports, schools, oil wells, shipyards, or ports.

Researchers powered their deep learning algorithms by combining large neural networks, known as convolutional neural networks (CNNs), and computers with large amounts of processing power. The result was a network that, when fed massive amounts of training data, can learn to identify and classify various objects from satellite imagery. By combining a number of these networks into what is called an ensemble, the algorithm can judge the results from each CNN to produce a final, improved result that is more robust than any single CNN.

This is how a team from Lockheed Martin, led by Mark Pritt, designed their deep learning algorithm for the challenge. Pritt explained to C4ISRNET, that he and his team developed their CNN using machine learning software and framework from online open source software libraries, such as Tensor Flow. Earning a top five finish, the algorithm designed by Pritt’s team achieved a total accuracy of 83 percent, and was able to classify 100 objects per second. Pritt said that with fully functioning algorithm, this software could take an image recognition task that takes a human an hour to complete and reduce the process to a few seconds.

The team’s algorithm excelled at identifying classes with distinctive features, and successfully matched nuclear power plants, tunnel openings, runways, tool booths, and wind farms with accuracies greater than 95 percent, but struggled with more indiscreet classes such as shipyards and ports, hospitals, office buildings, and police stations.

“Usually when you develop an algorithm its nice to see where it succeeds, but you actually learn the most where you look at where the algorithm fails or it doesn’t do well,” Pritt said. In trying to decipher why the algorithms struggled, Pritt said the competitors suggested that some objects simply don’t have any distinguishing features from the point of view of a satellite image for the algorithms to recognize.

“Maybe the most important ingredient you need for these new types of algorithm to work is the dataset because these algorithms require a great amount of data to train on,” Pritt explained. “It’s kind of analogous to the way a human will learn in childhood how to recognize things. You need lots of examples of what those things are and then you can start to generalize and make your own judgments,” he said.

But even with large amounts of training data that is correctly labeled, it is also possible the deep learning technology of today cannot reach the higher levels of intelligence to recognize nuanced differences. For example, Lockheed Martin’s algorithm confused shipyards and ports 56 percent of the time. Pritt said that people “look at an image and they can tell that it’s a port or a shipyard, they are usually looking at very subtle things such as if there is a ship in dry dock or if there is a certain type of crane present. They are looking for details in the image that are maybe higher level or more complicated than what these deep learning algorithms can do right now.”

However, the fact that these algorithms cannot do everything should not dismiss the significant contribution they could provide to the defense and intelligence community.

Hakjae Kim, IARPA’s program manager for the fMoW challenge, said the benefits of this technology could extend far beyond faster image processing. “I want to look at it more in the perspective that we can do things we weren’t able to do before,” Kim said. “Because its technology that we are now able to do x, y and z, there are more applications you can create because with the human power it is just impossible to do before.”

Kim and Pritt stressed managing expectations for CNN-based artificial intelligence.

“This is a real technology that will work, but it also has limitations. I don’t want to express this technology as a magic box that will just solve everything magically,” Kim said. “I don’t want the users in the field to get disappointed by the initial delivery of this technology and say ‘Oh, this is another technology that was oversold and this is not something we can use,” he added.

Part of managing our expectations for AI requires recognizing that although intelligence is in the name, this technology does not think and reason like humans. “A lot of the time we think that because we use the term AI, we tend to think these algorithms are like us, they are intelligent like us,” Pritt said. “And in someways they seem to mimic our intelligence, but when they fail we realize ‘Oh, this algorithm doesn’t really know anything, [it] doesn’t have any common sense.’”

So how are IARPA and Lockheed Martin working to improve their algorithms? For IARPA, Kim’s team is working on updating and maintaining their dataset to ensure algorithms have the most up to date information to train on, ultimately making the CNN-based algorithms easier to trust. “[S]ubtle changes in the area mess up the brains of the system and that system will give you a totally wrong answer,” Kim explained. “So we have planned to continuously look over the area and make sure the algorithm we are developing and reassessing for the government to test on and use to be robust enough for their application,” he furthered.

Work is also underway at American universities. Kim described how a team of researchers at Boston University are using the fMoW dataset and tested algorithms to create heat maps that visualize what part of the image algorithms are using to classify objects. They’ve found that sometimes it is not the object itself, but clues surrounding the object that aid most in classification. For example a “windmill that actually shows a shadow gives a really good indicator of what that object is,” Kim said. “Shadows show a better view of the object. A shadow is casting the side view of the object over on the ground, so [BU’s heat map algorithm] actually points out the shadow is really important and the key feature to make the object identified as a windmill.”

But don’t expect these algorithms to take away the jobs of analysts any time soon. “I think you still need a human doing the important judgments and kind of higher level thinking,” Pritt said. “I don’t think AI will take away our jobs and replace humans, but I think what we have to do is figure out how to use them as a tool and how to use them efficiently, and that of course requires understanding what they do well and what they do poorly,” he concluded.

So when the leaders of the Air Force’s Global Strike Command, Space Command, commander in Europe and Africa and special operations head all shared a stage at the Air Force Associations annual conference to discuss operations, its no surprise they chose to focus on how the service plans to leverage space assets to project power in the 21st century.

One reason space is so important, not just to the Air Force, but all military services, is that space assets enable multidomain operations that are becoming the norm in the modern warfare. Citing the Air Force’s successful strikes against Assad-regime chemical weapons manufacturing sites in Syria in April, Gen. Tod D. Wolters, commander of U.S. Air Forces in Europe and Africa, explained the mission was successful because “we had well-vetted and thorough multidomain operations.”

Another key reason the Air Force is doubling down on space is because the outcome of a conflict with a near-peer will largely depend on winning the battle in space. “If deterrence fails I am convinced … if we are up against a peer or near-peer we are going to have to fight for space superiority,” said Gen. John Raymond, commander of Air Force Space Command. “As the National Defense Strategy talks about, that’s going to require capability from multiple domains to come forward,” he added.

Raymond noted the need to control assets in space was prioritized in the National Security Strategy, which talks about “unfettered access to and freedom to maneuver in space as a vital national interest. That’s significant, it wasn’t in the last National Security Strategy.”

And unlike in previous endeavors beyond Earth’s atmosphere, the U.S. is no longer going it alone. Looking at the spectrum of operations “from humanitarian assistance and disaster relief on one end to kinetic on the other end, there is nothing that isn’t done without multidomain operations or integration of air, space and cyber together, and there is nothing we do not do without our allies,” Raymond said. “So by definition multidomain, [and command and control] has to be coalition friendly.”

Considering the need for U.S. partners in space and how the space environment has evolved, Raymond explained, “we haven’t needed to have partners in space before. It was a benign domain…. That is not the case today and we are working really hard to develop those partnerships that we enjoy in all other domains than space.”

But projecting power from space requires work to be done on terra firma by the U.S. and its allies.

Gen. Timothy Ray, commander of U.S. Air Force global strike command, explained “The ability to improve much faster at our bases and our power projection platforms absolutely has to be an integrated conversation. We talk about integrating air space and cyber we fundamentally have to master the integration of our bases.”

That’s why Lockheed Martin will begin a series of updates to the architecture’s ground control system following the initial launch, according to a statement from the company. These updates will give the Air Force get a head start on testing and operations before the majority of the constellation is in place.

The Air Force placed Lockheed Martin on contract in 2016 and 2017 to upgrade the existing Architecture Evolution Plan (AEP) Operational Control System (OCS) called GPS III Contingency Operations (COps) and M-Code Early Use (MCEU), respectively. Just as the GPS III satellites themselves are equipped with improved anti-jamming technology and more accurate signals transmission capability, the ground systems and software that control them need to be upgraded as well.

The Air Force has also worked with Raytheon on the estimated $6 billion Operational Control Segment program, often referred to as OCX. That program is expected to serve as the primary ground control system for the GPS III program but has been behind schedule. SpaceNews recently reported the target completion date for Block 1 of the program is June 2021. Block O, the launch and checkout system, was delivered in September 2017.

Lockheed Martin’s contingency program will allow the existing control system to support and integrate more powerful GPS III satellites. Modifications will support GPS III satellites in their position, navigation and timing missions, coordinating their movement with GPS IIR, IIR-M and IIF satellites already in orbit.

A second set of upgrades, known as the MCEU modernization program, will focus on the development of M-Code, a new advanced signal designed to improve anti-jamming and anti-spoofing capabilities. The program will improve the existing ground system and allow it to task, upload and monitor M-Code within the GPS constellation. In other words, MCEU modernization will help the Air Force integrate and test GPS III satellites into the current constellation earlier.

COps is on schedule for delivery in May 2019 and MCEU is scheduled for delivery in January 2020.

The organization opened the new floor July 15 at Hill Air Force Base in Utah. The center is now known as DISA Global West. The original operations watch floor, DISA Global Operations Command, headquartered at Scott AFB, Illinois will now be known at DISA Global East.

Opening a second watch floor improves the survivability and redundancy of global network operations, the DISA statement said, and allows for what’s described as 24/7 “Active-Active” operations. “Active-Active” means that DISA is continuously available to provide operational and security capabilities, even if one of the two operation centers goes down. This added layer of redundancy is viewed as critical in an increasingly hostile cyber environment.

“In a world where our cyber adversaries are becoming more and more sophisticated in their abilities to infiltrate networks and interfere with services, Active-Active operations allows DISA Global to reduce continuity risks and make the Defense Information Systems Network and DISA services more agile and responsive,” said Laura Williams, the agency’s Active-Active program manager.

While dividing the work force between locations introduces new difficulties to operations, the agency is confident the move will improve resiliency without compromising capability.

“It’s always a challenge when you have a geographically separated work force,” said Army Col. Lisa Whittaker, DISA Global commander said in the release. “But we are a tremendous team coming together for this effort. The teammates that work at DISA Global West are closely integrated with those at DISA Global East. They work in the same divisions, have the same leadership, and use the same tools, techniques, tactics, and procedures on a daily basis.”

One reason teammates are able to stay on the same page despite being over 1,300 miles apart is the agency’s use of an Enterprise Virtual Watch Desk, which provides organizations across the agency a common picture of the operational network.