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August 28, 2023 | International, Naval

Indian Navy to get 3 more MDL-Naval Group built Scorpene submarines through Buy (Indian) route

Some friendships remain strong and win-win forever, giving both the sides reason to smile. Prime Minister Narendra Modi’s visit to France as Guest of Honour.

https://www.aviation-defence-universe.com/indian-navy-to-get-3-more-mdl-naval-group-built-scorpene-submarines-through-buyindian-route/

On the same subject

  • The Army AI task force takes on two ‘key’ projects

    June 12, 2020 | International, Security

    The Army AI task force takes on two ‘key’ projects

    Andrew Eversden The Army's artificial intelligence task force is working on two key projects, including one that would allow unmanned vehicles in the air to communicate with autonomous vehicles on the ground, after securing new funding, a service official said June 10. Gen. Mike Murray, commander of Army Futures Command, said during a June 10 webinar hosted by the Association of the United States Army that the task force has moved forward on the projects through its partnership with Carnegie Mellon University, launched in late 2018 . First, the team is working on programs dedicated to unmanned-unmanned teaming, or developing the ability of air and ground unmanned vehicles to talk to one other. The other effort underway is on a DevSecOps environment to develop future algorithms to work with other Army systems, Murray said. He did not offer further detail. The task force force has fewer than 15 people, Murray said, and fiscal 2021 will be the first year that it receives appropriated funds from Congress. Much of the work the task force has done so far as been building the team. In response to an audience question, Murray said that the task force is not yet working on defending against adversarial machine learning, but added that leaders recognize that's an area the team will need to focus on. “We're going to have to work on how do we defend our algorithms and really, how do we defend our training data that we're using for our algorithms," Murray said. In order to train effective artificial intelligence, the team needs significant amounts of data. One of the first projects for the task force was collecting data to develop advanced target recognition capabilities. For example, Murray said, being able to identify different types of combat vehicles. When the work started, the training data for target recognition didn't exist. “If you're training an algorithm to recognize cats, you can get on the internet and pull up hundreds of thousands of pictures of cats,” Murray said. “You can't do that for a T-72 [a Russian tank]. You can get a bunch of pictures, but are they at the right angles, lighting conditions, vehicle sitting camouflaged to vehicle sitting open desert?” Murray also said he recognizes the Army needs to train more soldiers in data science and artificial intelligence. He told reporters in late May that the Army and CMU have created a masters program in data science that will begin in the fall. He also said that the “software factory,” a six- to 12-week course to teach soldiers basic software skills. That factory will be based in Austin, where Futures Command is located, and will work with industry's local tech industry. “We have got to get this talent identified I'm convinced we have it in our formations,” Murray said. https://www.c4isrnet.com/artificial-intelligence/2020/06/10/the-army-ai-task-force-takes-on-two-key-projects/

  • German military set to buy 20,000 encrypted radios for 1.35 bln euros -source

    December 8, 2022 | International, C4ISR

    German military set to buy 20,000 encrypted radios for 1.35 bln euros -source

    Germany's parliament will on Dec. 14 approve a 1.35-billion-euro purchase of 20,000 encrypted radios for its military, a person familiar with the matter said, with an option to buy another 14,000 radios for 1.5 billion euros.

  • How airmen can work together for persistent ISR

    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/

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