March 17, 2021 | International, Aerospace
The attacks by different types of threats was modeled on Iranian tactics. Now the US will get the same upgrades.
https://breakingdefense.com/2021/03/iron-dome-defeats-drones-rockets-in-test
June 9, 2024 | International, Aerospace
The H225 will replace the H155 and AS332 helicopters that have been in service with the German Federal Police for more than 20 years
October 5, 2020 | International, Aerospace, Naval, Land, C4ISR, Security, Other Defence
NAVY Northrop Grumman Systems Corp., Mission Systems Sector, Linthicum Heights, Maryland, was awarded a $100,798,804 fixed-price-incentive-fee and firm-fixed-price contract for follow-on production of Surface Electronic Warfare Improvement Program Block 3 electronic attack systems and hardware design modifications required for aircraft carrier and amphibious assault ship installation. This contract includes options, which if exercised, would bring the cumulative value of this contract to $1,164,529,315. Work will be performed in Baltimore, Maryland (55%); Tampa, Florida (6%); Andover, Massachusetts (5%); Chelmsford, Massachusetts (4%); Rochester, New York (3%); San Diego, California (3%); Los Angeles, California (2%); Winona, Minnesota (2%); Stafford Springs, Connecticut (2%); Glendale, Arizona (1%); Nashua, New Hampshire (1%); Elk Grove Village, Illinois (1%); White Marsh, Maryland (1%); Tucson, Arizona (1%); Chandler, Arizona (1%); Washington, North Carolina (1%); Woodridge, Illinois (1%); Richardson, Texas (1%); Minneapolis, Minnesota (1%); El Cajon, California (1%); Hiawatha, Iowa (1%); Littleton, Colorado (1%); Glendale, California (1%); and miscellaneous locations - each less than 1% (4%), and is expected to be completed by May 2023. If all options are exercised, work will continue through September 2026. Fiscal 2019 other procurement (Navy) (67%); and fiscal 2020 other procurement (Navy) (33%) funding in the amount of $100,798,804 will be obligated at time of award and will not expire at the end of the current fiscal year. This contract was competitively procured via the Federal Business Opportunities website with one offer received. The Naval Sea Systems Command, Washington, D.C., is the contracting activity (N00024-20-C-5519). (Awarded Sept. 30, 2020) EFW Inc., Fort Worth, Texas, is awarded a $35,801,006 five-year requirements type, firm-fixed-priced contract for repair of line-replaceable units in support of the V-22 aircraft. This is a five-year contract with no option periods. Work will be performed in Fort Worth, Texas (50%); and Talladega, Alabama (50%). Work is expected to be completed by October 2025. Annual working capital funds (Navy) will be used and funds will not expire at the end of the current fiscal year. No funds will be obligated at the time of award. One company was solicited for this sole-sourced requirement under authority 10 U.S. Code 2304 (c)(1), with one offer received. The Naval Supply Systems Command, Weapon Systems Support, Philadelphia, Pennsylvania, is the contracting activity (N00383-20-D-Y001). BAE Systems Land & Armaments L.P., Minneapolis, Minnesota, was awarded a $17,290,912 firm-fixed-price contract for the production of two 57mm MK 110 Mod 0 gun mounts and associated hardware. Work will be performed in Karlskoga, Sweden (93%); and Louisville, Kentucky (7%), and is expected to be completed by May 2023. Fiscal 2018 weapons procurement (Navy); and fiscal 2020 weapons procurement (Navy) funding in the amount of $17,290,912 will be obligated at time of award and $249,448 will expire at the end of the current fiscal year. In accordance with 10 U.S. Code 2304 (c)(1), this contract was not competitively procured; only one responsible source and no other supplies or services will satisfy agency requirements. The Naval Sea Systems Command, Washington, D.C., is the contracting activity (N00024-20-C-5300). (Awarded Sept. 30, 2020) Peraton Inc., Herndon, Virginia, is awarded a $13,891,979 cost-plus-fixed-fee, level of effort contract (N00030-21-C-0016) for program support services for the Navy's strategic weapons systems reentry subsystem. Work will be performed in Colorado Springs, Colorado (75%); Washington, D.C. (15%); Albuquerque, New Mexico (8%); Cape Canaveral, Florida (1%); and Omaha, Nebraska (1%). Work is expected to be completed by March 30, 2026. Contract will be awarded subject to the availability of funds. No funds will be obligated at the time of award. Once funding becomes available, contract will be funded as follows: fiscal 2021 research, development, test and evaluation funds in the amount of $7,214,639; and fiscal 2021 operations and maintenance (Navy) funds in the amount of $6,677,340, which will expire at the end of the current fiscal year. This contract is being awarded to the contractor on a sole-source basis under 10 U.S. Code 2304(c)(1) and was previously synopsized on the Beta.sam.gov (formally Federal Business Opportunities) website. Strategic Systems Programs, Washington, D.C., is the contracting activity. BAE Systems Land & Armaments L.P., Minneapolis, Minnesota, was awarded an $8,934,292 cost-plus-fixed-fee and firm-fixed-price order under previously awarded blanket ordering agreement N00024-19-G-5306 for engineering services, open, inspect and repair services and spare and component parts in support of the MK 110 MOD 0 gun mount. This order includes options which, if exercised, would bring the cumulative value of this contract to $23,400,781. Work will be performed in Louisville, Kentucky (50%); and Karlskoga, Sweden (50%), and is expected to be completed by December 2022. Fiscal 2020 weapons procurement (Navy) (92%); and fiscal 2018 weapons procurement (Navy) (8%) funding in the amount of $6,128,002 will be obligated at time of award, of which $495,948 will expire at the end of the current fiscal year. This order was not competitively procured in accordance with 10 U.S. Code 2304(c)(1); only one responsible source and no other supplies or services will satisfy agency requirements. The Naval Sea Systems Command, Washington, D.C., is the contracting activity (N00024-20-F-5301). (Awarded Sept. 30, 2020) ARMY AstraZeneca, Gaithersburg, Maryland, was awarded a $60,000,000 firm-fixed-price contract to manufacture AZD7442, a combination antibody product intended to prevent or treat clinical effects of SARS-CoV-2, for a minimum of 100,000 treatment courses. Work will be performed in Gaithersburg, Maryland, with an estimated completion date of June 30, 2021. Fiscal 2020 Army general funds in the amount of $30,000,000 were obligated at the time of the award. U.S. Army Contracting Command, Aberdeen Proving Ground, Maryland, is the contracting activity (W911QY-20-C-0119). (Awarded Sept. 30, 2020) CORRECTION: The contract announced on Sept. 28, 2020, for Tatum Excavating Co. Inc., Texarkana, Texas (W9126G-20-F-0768), for $10,000,000, was announced with an incorrect award date. The correct award date is Sept. 29, 2020. CORRECTION: The contract announced on Sept. 29, 2020, for University of South Dakota, Vermillion, South Dakota (W9128F-20-D-0059), for $12,800,000, was announced with an incorrect awardee. The correct awardee is South Dakota State University, Brookings, South Dakota. MISSILE DEFENSE AGENCY Lockheed Martin Rotary and Mission Systems, Moorestown, New Jersey, has been awarded a $35,582,832 sole-source, hybrid (cost-plus-fixed-fee, firm-fixed-price) contract (HQ0851-21-C-0001) under Foreign Military Sales (FMS) Case JA-P-NCO to the government of Japan. Under this contract, Lockheed Martin will perform Aegis FMS Baseline J7.B development and SPY-7(V) 1 radar production, integration and test planning support. The work will be performed in Moorestown, New Jersey. The period of performance is from Oct. 2, 2020, through July 31, 2021. Funds from the government of Japan in the amount of $35,582,832 are being obligated at the time of award. The Missile Defense Agency, Dahlgren, Virginia, is the contracting activity (HQ0851-21-C-0001). AIR FORCE Wolverine Supply Inc., Wasilla, Alaska, has been awarded an $8,649,500 firm-fixed-price contract for repair of the Blackstart Generator. This contract provides for repair of the Blackstart Generator at the Eielson Air Force Base central heat and power plant. Work will be performed at Eielson AFB, Alaska, and is expected to be complete by Sept. 22, 2022. This award is the result of a competitive acquisition and four offers were received. Fiscal 2020 operations and maintenance funds in the full amount are being obligated at the time of award. The 354th Contracting Squadron, Eielson AFB, Alaska, is the contracting activity (FA500420C0015). (Awarded Sept. 30, 2020) * Small business https://www.defense.gov/Newsroom/Contracts/Contract/Article/2370617/source/GovDelivery/
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