Japan to relax export curbs to allow overseas sales of joint jet fighter

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  September 21, 2021 | International, Aerospace, Naval, Land, C4ISR, Security

  Contracts for September 20, 2021

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  July 14, 2020 | International, Aerospace

  Air Force to Add 12 Weapons Systems for AI/ML-Informed Predictive Maintenance This Year

  The U.S. Air Force is to add a dozen weapons systems to its Enhanced Reliability Centered Maintenance (ERCM) model that employs artificial intelligence/machine learning (AI/ML) for predictive maintenance. Those systems are the Boeing [BA] F-15 fighter, B-52 bomber, RC-135 reconnaissance plane, C-17 transport, and A-10 Thunderbolt II close air support aircraft, the Lockheed Martin [LMT] AC/MC-130 gunships, F-16 fighter, and HH-60 helicopter, the Bell [TXT] and Boeing CV-22 tiltrotor, the Northrop Grumman [NOC] RQ-4 Global Hawk and the General Atomics‘ MQ-9 Reaper. “We have a couple of different initiatives under what we would call the umbrella of predictive maintenance,” Air Force Lt. Gen. Warren Berry, the service's deputy chief of staff for logistics, engineering and force protection, said during a July 9 Mitchell Institute for Aerospace Studies' Aerospace Nation virtual discussion. “One is Condition Based Maintenance Plus [CBM+]. We have three weapons systems in there right now: the C-5, the KC-135, and the B-1. They've been doing it for about 18 to 24 months now, and we're starting to get some real return on what it is that the CBM+ is offering us. The other element is called Enhanced Reliability Centered Maintenance [ERCM], which is really laying that artificial intelligence and machine learning on top of the maintenance information system data that we have today and understanding failure rates and understanding mission characteristics of the aircraft and how they fail, and then laying that into the algorithms that then tell us when parts are likely to fail based on failure rates and the algorithms we plug in.” “We're in the process of adding another 12 weapons systems under the ERCM umbrella this calendar year,” Berry said. Defense Daily has asked Air Force Materiel Command (AFMC) for the names of the 12 systems. AI/ML is to assume a significant role in predictive maintenance for the 11 combatant commands (COCOMs). In April last year, the Pentagon said that the new Joint Artificial Intelligence Center (JAIC) had delivered its first product, a predictive Engine Health Model (EHM) maintenance tool for Sikorsky [LMT] Black Hawk helicopters, to U.S. Special Operations Command's 160th Special Operations Regiment (SOAR) for use with SOAR's MH-60 helicopters. JAIC said that its Joint Logistics Mission Initiative (MI), one of six JAIC AI projects, is working “to develop a repeatable, end-to-end AI ecosystem” to bring EHM to scale across the Black Hawk fleet. EHM, developed in partnership with Carnegie Mellon University, “predicts the probability of an engine hot start so decision-makers can consider next steps,” including replacing the engine or holding it back for training missions instead of deployments in high-risk missions, Army Col. Kenneth Kliethermes, JAIC's Joint Logistics MI lead, said in a recent JAIC blog post. Another JAIC mission initiative, the Joint Warfighting MI, “is working with several COCOMs to build, test, and expand its Smart Sensor, a video processing AI prototype that rides on unmanned aerial vehicles and is trained to identify threats and immediately transmit the video of those threats back to manned computer stations for real-time analysis,” according to the JAIC blog post. Army Col. Bradley Boyd, the lead for the Joint Warfighting MI, said that the Smart Sensor could lead to “a dramatic reduction in the amount of data that has to be pushed back for a human to cull through.” “Instead of staring at one video feed and hours and hours of trees and rocks and nothing happening, that person can instead be monitoring 10 video feeds because they are only seeing the stuff that really matters,” Boyd said in the JAIC blog post. https://www.defensedaily.com/air-force-add-12-weapons-systems-ai-ml-informed-predictive-maintenance-year/army/

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  October 9, 2019 | International, C4ISR

  How airmen can work together for persistent ISR

  By: Brig. Gen. Gregory Gagnon and Lt. Col. Nishawn Smagh There is always a next war. Great power competition is here. Now is the time, while the United States maintains a position of strength, to ensure we are not outmatched, out-thought, or out-witted. Rapidly and realistically positioning the Intelligence, Surveillance, and Reconnaissance enterprise for first-mover advantage in today's data-driven environment is beginning with purposeful urgency. The past paradigm: crew-to-aircraft model During our careers, the Air Force ISR enterprise grew in both capability and capacity. In the late 1990s, the Air Force operated an ISR enterprise dominated by manned aircraft, each with their own specialized team operating unique systems that turned data into initial intelligence. Only a few organizations could turn raw airborne sensor data into intelligence in near-real time. We were only beginning to move data to the analyst, versus deploying the analyst to the data. As battlefield demand of ISR grew, we scaled up. We were fortunate to help build and execute airborne intelligence operations on a global scale, connected via a global network — we called them “reachback” operations. Reachback operations were the first step in transmitting ISR sensor collection across the globe in seconds. Even today, few nations can conduct this type of ISR operational design. The enterprise has continued to advance, achieving fully distributed operations around the world. We also made it possible to remove humans from aircraft, allowing missions to fly nearly three times longer and expand the data available to exploit. Correspondingly, the Air Force increased the number of organizations that could accept data and create intelligence. Following 9/11, our nation's needs changed; the fight necessitated the Air Force grow its capacity to deliver intelligence for expanded operations in the Middle East. We bought more unmanned vehicles, trained more ISR Airmen, and created more organizations to exploit data. Collection operations were happening 24/7 and most sorties required multiple crews to fly, control sensors and turn collection tasks into intelligence. As reachback operations grew, they became the Distributed Common Ground System and developed the ability to exploit aircraft sensor data. This growth was significant, but at the tactical level we employed the same crew model and simply grew at scale. This resulted in manpower growth, but also in disparate, distributed crews working similar tactical requirements with little unity of effort or larger purpose. This limited the ability of ISR airpower to have broader operational effects. While suitable for counter-terrorism, history tells us this approach is ill advised for great power conflict. Observe and orient: the data explosion and sense-making The traditional crew-to-aircraft model for exploitation must fast forward to today's information environment. The Pentagon has shifted its guidance to this new reality. The Defense Department recently declared information a seventh core function, and the Air Force's formal ISR flight plan maps a course for digital-age capabilities to turn information into intelligence. This “sense-making” must be able to handle both the complexity of a diverse information environment and scale to contend with an exploding volume of data. Access to expanded data sets, from diverse collection sources and phenomenology, is near and urgently needed. The Department's focus on artificial intelligence and machine learning in this realm remains stable and necessary. The next step is to retool how we task, organize, and equip both intelligence collection and analytic crews. As the Pentagon focuses on open architectures, artificial intelligence and machine learning, and data standards, the field is rapidly moving out. Air Combat Command , the Air Force lead command for ISR, is attacking the crew-to-aircraft model to test a sensor-agnostic approach using multiple data sources to address intelligence requirements. Cross-functional teams of Airmen are now assigned broader operational problems to solve, rather than a specific sensor to exploit. This will change joint and service collection management processes. ACC is tackling this future. We are supporting Air Force commanders in Europe and the Pacific with a pilot project that allows Airmen to explore these sensor-agnostic approaches. An additional element to our future success is partnering with our joint and allied partners, as well as national agencies, to bring resources, tools, and insights to bear. As we field the open architecture Distributed Common Ground System, we are shifting the focus from airmen operating specific sensors to airmen leveraging aggregate data for broader analysis. Headquarters Air Force and ACC are installing technologies to ensure readiness for the future ISR enterprise. Cloud technology paired with artificial intelligence and machine learning promises to speed human-machine teaming in generating intelligence across warfighting domains at the speed and scale necessary to inform and guide commanders. Underpinning this effort is a new data strategy and agile capability development for rapid prototyping and fielding. The Defense Department and the Air Force must continue to prioritize this retooling. Our adversaries see the opportunities; this is a race to the future. Situational awareness in the next war will require the development and fielding of AI/ML to replace the limited and manpower-intensive processes across the Air Force ISR enterprise. Employing AI/ML against repetitive data exploitation tasks will allow the service to refocus many of its ISR Airmen on AI/ML-assisted data analysis and problem solving. ISR and multi domain command and control ... enabling decide and act A headquarters-led initiative, with eyes toward a joint capability, is the creation of a collaborative sensing grid that operates seamlessly across the threat spectrum. Designs call for a data-centric network of multi domain platforms, sensors, and airmen that work together to provide persistent ISR. Equipped with manned and unmanned platform sensors capable of computing via AI/ML, these capabilities will link commanders to real-time information, plus tip and cue data from sensors-to-sensors, joint commanders, and weapons. This collaborative sensing grid is a foundational element for multi domain command and control . The vision of MDC2 is to outpace, outthink and outmaneuver adversaries. Creatively and rapidly applying new technology to operational problems is a long-held characteristic of airmen. Our DCGS airmen are no different. Non-material solutions deserve as much attention as hardware. This pilot project is our vanguard initiative to prepare for rapidly changing future systems environments. https://www.c4isrnet.com/opinion/2019/10/08/how-airmen-can-work-together-for-persistent-isr/