BlueHalo Awards Mercury Production Agreement to Provide Digital Signal Processing Hardware for U.S. Space Force Satellite Control System

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  July 15, 2019 | International, Other Defence

  Army to Receive 7.62mm Squad Marksman Rifles as Early as Next Year

  By Matthew Cox Heckler & Koch Defense Inc. will soon begin delivering thousands of 7.62mm squad-designated marksman rifles to the Army to give infantry and other close-combat units a better chance of penetrating enemy body armor. H&K will deliver "between 5,000 and 6,000" variants of the G28 rifle, which the Army plans to issue as its new squad designated marksman rifle (SDMR), according to a July 12 H&K news release. Under the agreement, the rifles will be manufactured by H&K in Oberndorf, Germany, and will begin to arrive in the H&K-USA facility in Columbus, Georgia, early next year, according to the release. Once there, H&K-USA workers will install scopes and mounts purchased by the Army under a separate agreement. "This is a significant achievement for Heckler & Koch," H&K-USA's chief operating officer, Michael Holley, said in the release. "The HK SDMR system will add much-needed capabilities to virtually every squad in the Army. We are honored by this opportunity." The new SDMRs are part of an interim effort to make squads more lethal ahead of the Army's fielding of the Next-Generation Squad Weapon system sometime in 2022, service officials have said. In May 2017, Army Chief of Staff Gen. Mark Milley told Senate Armed Services Committee members that the service's current M855A1 Enhanced Performance Round will not defeat enemy body armor plates similar to the U.S. military-issue rifle plates such as the Enhanced Small Arms Protective Insert, or ESAPI. As a short-term fix, the Army selected the G28 as its M110A1 Compact Semi-Automatic Sniper System in 2016, to be used with the service's new 7.62mm enhanced performance round to give squads more penetrating power. In the past, the Army relied on the Enhanced Battle Rifle, or EBR, 14 -- a modernized M14 equipped with an adjustable aluminum stock with pistol grip, scope and bipod legs -- to fill the growing need by infantry squads operating in Afghanistan to engage enemy fighters at longer ranges. But the EBR is heavy, weighing just under 15 pounds unloaded. The M110A1 weighs about 11 pounds. In the long term, the Army is working with gunmakers to develop the new Next Generation Squad Weapon (NGSW) that is slated to fire a special, government-produced 6.8mm projectile that promises higher velocities at greater ranges, service officials say. The program is being designed to produce an automatic rifle version to replace the M249 squad automatic weapon and a carbine version to replace the M4 carbine. Army officials said recently that they expect to begin receiving prototypes of the NGSW in July and August and that the weapon could be fielded to units beginning in late fiscal 2020. https://www.military.com/daily-news/2019/07/12/army-receive-762mm-squad-marksman-rifles-early-next-year.html

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  October 5, 2021 | International, Aerospace, Naval

  ‘Hard decisions’: Navy planning cuts to Boeing’s Super Hornet upgrade program

  The military wants to put more resources into a next-generation fighter, and they’re looking at the Super Hornet programs for money.

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  June 19, 2019 | International, C4ISR, Other Defence

  Can AI help limited information have endless potential?

  By: Kelsey D. Atherton Humans are remarkably good at choosing to act on limited information. Computers, less so. A new DARPA program wants to train artificial intelligence to process and evaluate information like humans do, and produce actionable results on far smaller datasets than presently done. It's a program of such important DARPA's giving it VIP status, or a least VIP as an acronym: Virtual Intelligence Processing. “Successful integration of next-generation AI into DoD applications must be able to deal with incomplete, sparse and noisy data, as well as unexpected circumstances that might arise while solving real world problems,” reads a solicitation posted June 14. “Thus, there is need for new mathematical models for computing leading to AI algorithms that are efficient and robust, can learn new concepts with very few examples, and can guide the future development of novel hardware to support them.” To create these mathematical models, DARPA wants partners to look inward, creating AI inspired by the robust and massive parallelism seen in the human neocortex. If it is the architecture of the brain that makes humans so especially skilled at processing information quickly, then it is an architecture worth studying. “In order to reverse engineer the human brain,” the solicitation continues, calmly, “we need to apply new mathematical models for computing that are complete and transparent and can inform next-generation processors that are better suited for third-wave AI.” It is DARPA's nature to inject funding into problem areas it sees as both yielding future results and not presently served by the market, and this is not different. The solicitation explicitly asks for mathematical models that have not already been the focus of AI development. It's also looking for models that can inform the development of future hardware, rather than programs that can run on existing machines. DARPA is interested in how the hardware works in simulation, but wants partners to hold off on actually making the hardware for the model. So, the plan goes: create a mathematical model, inspired by brains, to process information on a small and limited data set, and then design it for hardware that doesn't exist yet. Easy as that sounds, the solicitation also asks proposers to talk about the limitations of the algorithms when applied to military tasks, and specifically limitations related to accuracy, data, computing power and robustness. Working from limited information is an expected future of military machines going forward. Between electronic warfare, denied environments and the very nature of battlefield events as rare and hard to record moments, doing more with on-board processing of limited data should enable greater autonomy. Even in the rare case where a weapon system transmits data back for algorithm refinement, that data set will be orders of magnitude smaller than the big data sets used to train most commercial machine learning tools. Should a proposer's idea be accepted and they follow through both Phase 1 and Phase 2 of the project, the total award is set at $1 million. A tidy sum, for anyone who can figure out the math to make a future computer run on sparse information as effectively as a human brain. https://www.c4isrnet.com/artificial-intelligence/2019/06/18/can-brain-inspired-ai-run-on-lean-data/