June 18, 2024 | International, Land, C4ISR
The agreement covers the initial provision of over 3,500 SquadNet tactical radios and around 2,500 radios from the SYNAPS product family.
January 1, 2024 | International, Aerospace
December 26, 2023 Defense and Security Thailand 2023 – IAI will supply 6 of its advanced Sea Mini-POP sensor payloads to the Royal Thai Navy. These payload upgrades will enhance...
January 17, 2019 | International, Aerospace
By: Aaron Mehta WASHINGTON — The long-delayed Missile Defense Review, which will be formally introduced by President Donald Trump at the Pentagon Thursday, will call for research and investments to ensure America's security for the next several decades: laser technology, the F-35 as an ICBM killer, and potentially putting interceptors in space. Trump will roll out the report at 11 a.m. Thursday as part of his third visit to the Pentagon since taking office. Expected to attend the rollout is a who's who of national security officials, including vice president Mike Pence, national security adviser John Bolton; Acting Secretary of Defense Patrick Shanahan; Air Force Secretary Heather Wilson; Army Secretary Mark Esper; Pentagon policy head John Rood; Undersecretary of Defense for Acquisition and Sustainment Ellen Lord; Pentagon technology head Mike Griffin; and Rep. Mike Turner of Ohio, a leading advocate for missile defense. A senior administration official, speaking to reporters ahead of the report's release, confirmed a number of new technologies that Defense News has learned are highlighted in the report. The official told reporters that overall, the review looks at “the comprehensive environment the United States faces, and our allies and partners face. It does posture forces to be prepared for capabilities that currently exist and that we anticipate in the future.” It's been a long road for the MDR to finally emerge. Pentagon officials originally said the document would be released in late 2017 — then February, then mid-May and then late in the summer. In September, Rood, who as undersecretary of defense for policy is the point man for the MDR, indicated the report could come out in a matter of weeks. And in October, Shanahan, then the deputy secretary of defense, said the document had been done “for some time.” There is also widespread speculation in the missile defense community that the review has been delayed, at least in part, because of the warmed relations between the Trump administration and North Korea. Notably, the mid-May time frame for release, which was floated by Shanahan in April, lined up President Donald Trump's planned meeting in Singapore with North Korean leader Kim Jong Un. While that meeting was canceled and then eventually happened in June, there was a sense the Pentagon did not want to do anything that could jeopardize those talks, such as releasing a report discussing how the U.S. could counter North Korean capabilities. Ironically, Trump will be rolling the report out just hours before a high-level North Korean delegation is expected to arrive in Washington for talks with the administration. However, Sung-Yoon Lee, a Korean expert with Tufts University‘s Fletcher School, doesn't expect that to impact any negotiations. “North Korea has the upper hand and is playing hard to get,” Lee said, and so won't make a big deal out of the MDR's statements on North Korea. “Their propaganda machinery at home may issue a statement a couple of days later, but [lead North Korean official Kim Yong Chol] would be foolish to address it while he's in D.C," he added. Technological changes Much of the technology discussed in the MDR will require many years of development, and in some cases will never come to fruition. But the following points give a good sense of the let's-try-everything approach the Pentagon is putting forth with the report: Turn the SM-3 and F-35 into ICBM killers: The SM-3 Block IIA ship-launched interceptor is designed for dealing with regional threats. But the Pentagon intends to test the weapon as a counter-ICBM system in 2020, as part of a goal of creating an extra layer of protection for the homeland. In essence, the department wants to offer as many options as possible, scattered around the globe, for making sure nothing gets through the safety net. The department has previously said the F-35 could be used in some capacity for missile defense, but the MDR calls for the testing and development of a new or modified interceptor which could shoot down a ballistic missile in the boost phase; expect early R&D funding for such a weapon to be in the FY20 budget request. There is also the possibility of using the F-35, equipped with its array of sensors, to hunt and track mobile missile units, which is a key part of North Korea's doctrine. Lasers on drones: The idea of using directed energy weapons, more commonly known as lasers, to take out a missile in the boost phase is not new, but it has received a boost in the past year in comments from technological leaders inside the building. In theory, putting a drone equipped with a laser high in the air at around 60,000 feet would keep it safe from any missile defense systems, while providing overwatch on potential launch sites. However, this idea feels more far-flung than others, in part because both the scaled up laser that would be needed for such capabilities has yet to be invented, let alone paired with a system that would be able to stay that high for long periods of time. In the meantime, DoD is developing a low-power laser demonstrator to evaluate and test what technologies would be needed to make such a system a reality, despite the fact that airborne laser weapons are perhaps the hardest directed energy system to develop. Space-based sensors: In the FY19 defense authorization bill, Congress required the missile defense agency to fully study and prototype ways to increase the space-based sensor layer. It's been another focus area for Griffin during his time in the Pentagon. “A space-based layer of sensors is something we are looking at to help give early warning, tracking and discrimination of missiles when they are launched,” the administration official said. “We see space as an area that's very important as far as advanced, next-level capabilities that will help us stay ahead of the threat.” Just what that layer looks like, however, remains to be seen. Expect some form of disaggregated architecture, relying on many smaller systems rather than the expensive, highly-capable systems that the U.S. has traditionally relied upon. Hosting sensor payloads on commercial satellites could also be in play. The hope is to demo some form of space-based sensor layer by early in the 2020s. Space-based interceptors: Perhaps the most controversial of the ideas being considered in the document comes from the idea of having interceptors placed in orbit to take out ballistic missiles. Picture a satellite equipped with 10 rockets that, when triggered by the sensor net, can target and launch against an incoming missile. The MDR does not call for investment in space-based interceptors at this point. Instead, the department will launch a study, lasting perhaps six months, to look into the most promising technologies and come up with estimates for cost and time; after the study is done, the department will look to move forward if it makes sense. But don't expect lasers in space anytime soon, with the administration official saying nothing has been determined, only that “we're going to study it and we'll see whether or not it's feasible.” Full article: https://www.defensenews.com/breaking-news/2019/01/17/space-based-interceptors-and-drones-with-lasers-the-pentagons-missile-defense-review-wish-list-revealed
March 23, 2020 | International, Land, C4ISR
A modern mechanized military lives or dies by maintenance. But what if a computer could warn you when your weapons and vehicles were about to break, so you could fix them before they ever let you down? By SYDNEY J. FREEDBERG JR. WASHINGTON: Raytheon is working with the military on multiple pilot projects for AI-driven predictive maintenance. What's that? Traditionally, military mechanics spend a huge amount of time on what's called preventive maintenance: They carry truckloads of spare parts to war, they consult historical tables of roughly how often certain parts wear out or break down, and they preemptively crack open the access hatches to check those parts on a regular basis. The idea behind predictive maintenance is to feed all that historical data into a machine learning algorithm so it can tell maintainers, vehicle by vehicle and part by part, when something is likely to fail. It's a tremendous technical challenge that requires scanning in years of old handwritten maintenance forms, downloading digital records, and then constantly updating the database. Ideally, you want up-to-the-minute reports on things like engine temperature and suspension stress from diagnostic sensors installed in frontline vehicles. You need to account not only for what kind of equipment you're operating, but how hard it's running for a particular mission and even where in the world it's operating, because environmental conditions like heat, moisture, dust, and sand make a huge difference to wear and tear. And you can't just push out a single software solution and call it done. You have to constantly update your data so the algorithm can continue to learn, evolve, and adapt to different situations. But, Raytheon's Kevin Frazier and Butch Kievenaar told me, artificial intelligence and machine learning have advanced dramatically over just the last five years. Now Raytheon – a long-established defense contractor – is partnered with a flock of niche innovators to make it happen. Currently, they told me, Raytheon is already conducting or about to launch several multi-month pilot projects, seeking to prove the technology's value to the military: For the Army, they're working with a commercial partner on the M2 Bradley Infantry Fighting Vehicle, the mainstay armored troop transport of the heavy combat brigades, and the hulking M88 Hercules, a tracked “armored recovery vehicle” designed to tow broken-down battle tanks back for repair, if necessary under enemy fire. For the V-22 Joint Program Office – which supports the Osprey tiltrotor for the Marines, Air Force Special Operations Command, and now the Navy – they're working on the V-22's collision-avoidance radar, a Raytheon product. And across their customer base, they're looking at ways to do predictive maintenance on the many complex components Raytheon provides for a host of programs. How does this work? Let's hear from Kevin and Butch in their own words (edited for clarity and brevity from a highly technical 50-minute interview): Q: What kinds of problems can this technology help the military solve? Kevin: Right now, maintenance is conducted either on a scheduled timeline or when something breaks. What we are trying to do is replace that one piece because you know it's about to wear out and prevent it from breaking. Butch: One of the biggest things is you've got to understand what mission you're trying to achieve. If I'm trying to answer platform readiness questions, then I have to have certain data that's related to that topic. If I am trying to do supply chain analysis, I'm asking questions about where are critical parts and what size stockages we have to have to reduce turnaround time. So I'm answering a different question, and I'm looking at a different data set. So the key to setting all this up is what you do on the front end with your data to give the data scientists so that we can refine the algorithm appropriately. Q: AI/ML requires a lot of data. Is that data really available for all these different military systems? Kevin: It is. It's in different states. Some vehicles have sensors on them. Some do self-diagnostics. Some of the older equipment, especially the support equipment, doesn't have any sensors on them — but they all have files. They all are in the maintenance system, so the data exists. Data doesn't have to purely digital. It does have to be digitized at some point, but it doesn't necessarily have to start being digital. It could be maintenance logs that are hand-written, or the operator of a particular vehicle does a walk around and does an inspection report, writes that up — that's something that you actually can scan and input. Now we can add so many different types of data that your whole data environment becomes much richer. It helps you get to that algorithm — and then to continue to take in that data and refine that model. You're still recording that data and getting data from both handwritten and digital sources to update your model and tune it, so that you're just that much more accurate. Butch: What we're talking about is discrete algorithms solving for discrete problem sets. You look at the environment, and what the algorithm does is it learns. You keep ingesting data. You can get it a bunch of different ways so your analytical tool continues to learn, continues to refine. I can do a physical download from the vehicle, or scan maintenance records, or get it all fed off of a downloader that automatically feeds to the cloud. It can be as fast as we can automate the process of that piece of equipment feeding information back. For the Army and the Air Force especially, there is sufficient data over the last 15 that pertains to the impacts of combat. And we have it for different environments that you can then use to help train and refine the algorithms that you're using as it learns. Kevin: You have to understand the impacts the environment has on how the vehicle is functioning and what type of a mission you're doing, because that will cause different things to wear out sooner or break sooner. That's what the AI piece does. The small companies that we partner with, who are very good at these algorithms, already do this to some extent in the commercial world. We're trying to bring that to the military. Butch: The really smart data scientists are in a lot of the smaller niche companies that are doing this. We combine their tools with our ability to scale and wrap around the customer's needs. These are not huge challenges that we're talking about trying to solve. It is inside the current technological capability that exists. We have currently several pilot programs right now to demonstrate the use cases, that this capability that actually works. https://breakingdefense.com/2020/03/raytheon-ai-fix-that-part-before-it-breaks