QinetiQ, Inzpire and BAE Systems Deliver Next Synthetic Training Concept Demonstration to the Royal Navy

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  10 mars 2024 | International, Terrestre

  Missile Defense Agency won’t brief public on budget request

  The Missile Defense Agency director says he is working on a way to share the organization's budget details in lieu of its usual Pentagon rollout.

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  27 mai 2020 | International, Aérospatial

  Leonardo AW159 Wildcat helicopter conducts first successful firings of Thales ‘Martlet’ Lightweight Multirole Missile (LMM)

  London May 26, 2020 - Leonardo and Thales are proud to announce the first successful firings of the Thales ‘Martlet' Lightweight Multirole Missile (LMM) from Leonardo's AW159 Wildcat helicopter. The firings were conducted as part of the UK MoD's Future Anti-Surface Guided Weapon (FASGW) programme and demonstrated the integration of the Martlet onto the AW159 platform. This represents a major milestone for the programme and will enable this high-end capability to enter service with the Royal Navy later this year. The firing trials were conducted from 27th April to 21st May 2020 and despite the current COVID-19 situation, Leonardo and Thales were able to support the UK Ministry of Defence by completing this critical activity. All of the teams involved had to adopt strict distancing procedures, in some cases having to find new ways of working, in order to make sure that the trials could go ahead. It is a testimony to the professionalism of those involved that these trials were successfully completed under such challenging and novel circumstances. “This major milestone demonstrates that the combination of the AW159 Wildcat and Martlet missile will be a flexible and effective tool for the Royal Navy. Next year the Wildcat fleet will embark on Carrier Strike Group missions with HMS Queen Elizabeth on its maiden operational deployment. As the only British company to design and manufacture helicopters on-shore, we're extremely proud to be equipping the UK Armed Forces with world-beating sovereign capabilities.” said Nick Whitney, Managing Director of Leonardo Helicopters (UK). “The successful live firings of the Thales LMM Martlet from the AW159 Wildcat is a key milestone in the programme, delivering a significant step-change in capability for the platform. LMM Martlet will ensure that the Wildcat has the best-in-class offensive capability to protect HMS Queen Elizabeth and her task group during her maiden operational deployment next year. With each platform capable of carrying up to 20 Martlet, the Wildcats deployed with the task group will be a significant deterrent to anyone wishing to interfere with UK interests.” said Philip McBride, General Manager, Integrated Airspace-protection Systems, Thales UK. In July 2014, Leonardo signed a contract with the UK Ministry of Defence to integrate, test and install the MBDA Sea Venom (heavy) and Thales LMM (light) missile systems onto Royal Navy AW159 Wildcat helicopters, a programme called Future Anti Surface Guided Weapon (FASGW). The FASGW (light) part of the programme has now seen the LMM, with its associated launcher and airborne laser guidance unit, successfully integrated into the Leonardo AW159 Wildcat sensor, displays and avionics systems. The LMM provides a step-change in capability for the Royal Navy which, in the maritime environment, faces a major challenge in engaging smaller, fast-moving, asymmetric threats, due to their high mobility, their small thermal and radar signatures and the severe background clutter encountered. The LMM is capable of surmounting these issues where traditional electro-optic and radar guidance systems do not provide the certainty of hit required. On-board the AW159 Wildcat platform, the LMM Martlet could also allow operators to engage air targets such as UAVs and other maritime helicopters. The launchers are mounted to the AW159 via the new Leonardo Weapon Wing, developed at the Company's design and manufacturing facility in Yeovil and first trialled last year. Each weapon wing will be able to carry either ten Martlet or two Sea Venom missiles and generates additional lift for the helicopter in forward flight, reducing demands on the main rotor. The twin-engine multi-role AW159 is able to conduct missions ranging from constabulary to high end warfighting where it has the capability to autonomously detect, identify and attack targets on land and at sea, including submarine threats. The high-performance platform has state-of-the-art systems, including a Leonardo Seaspray multi-mode electronically-scanning (E-scan) radar, and integrated electronic warfare Defensive Aids Suite (DAS). Over 50,000 flight hours have been logged by the helicopter. The AW159 has also been chosen by the British Army, the Republic of Korea Navy and the Philippine Navy as a new maritime operator of the helicopter. About Leonardo Leonardo, a global high-technology company, is among the top ten world players in Aerospace, Defence and Security and Italy's main industrial company. Organised into five business divisions, Leonardo has a significant industrial presence in Italy, the United Kingdom, Poland and the USA, where it also operates through subsidiaries such as Leonardo DRS (defense electronics), and joint ventures and partnerships: ATR, MBDA, Telespazio, Thales Alenia Space and Avio. Leonardo competes in the most important international markets by leveraging its areas of technological and product leadership (Helicopters, Aircraft, Aerostructures, Electronics, Cyber Security and Space). Listed on the Milan Stock Exchange (LDO), in 2019 Leonardo recorded consolidated revenues of €13.8 billion and invested €1.5 billion in Research and Development. The Group has been part of the Dow Jones Sustainability Index (DJSI) since 2010 and became Industry leader of Aerospace & Defence sector of DJSI in 2019. Contact Ph. +39 0632473313 (Press Office) Ph. +39 0632473512 (Investor Relations) leonardopressoffice@leonardocompany.com ir@leonardocompany.com About Thales Thales is a global technology leader combining a unique diversity of expertise, talent and cultures. Our architects design and deliver decisive technologies for decisive moments in five markets: Defence & Security, Digital Identity and Security, Aerospace, Space, and Ground Transportation. In 2018, the company generated revenues of €19 billion with 80,000 employees in 68 countries. With its 30,000 engineers and researchers, Thales has a unique capability to design, develop and deploy equipment, systems and services that meet the most complex security requirements. Thales in the UK is a team of over 6,500 experts, including 4,500 highly skilled engineers, located across 10 key UK sites. In 2018, Thales UK's revenues were around £1.3 billion. Each year Thales invests over £575 million into its UK supply chain, working with over 2,000 companies. With a heritage of over 130 years, Thales in the UK understands the importance of developing skills for the future, which is why they have over 400 apprentices and graduates across the UK. Thales is committed to supporting its people, and continuously developing talent, and highly skilled experts. www.thalesgroup.com > Lightweight Multirole Missile - LMM (Martlet) is a new lightweight, precision strike, missile, which has been designed to be fired from airborne and ground tactical platforms in surface, ground attack and air defence roles; thus the multirole element of the name. The missile, sealed in its canister and designed to be maintenance free for 15 years' storage, consists of a two-stage motor, warhead and dual mode fuse, together with guidance electronics and a highly accurate control actuator system. A combined fragmenting and shaped charge warhead provides proven lethality against a wide range of conventional and asymmetric light skinned and armoured threats. The unique LMM laser guidance beam, generated from a sophisticated Laser Transmitter Unit (LTxU), projects low power coded signals direct to the LMM in flight thus ensuring precision engagement, command override and immunity against countermeasures. In the naval domain, the system has been designed to counter the challenging threats ranging from Jet Skis and Fast Inshore Attack Craft (FIAC) to larger maritime combatants. In 2019 Thales and the Royal Navy conducted a series of successful LMM (Martlet) ship-launched firings from a Type 23 frigate against a representative target set. These firings confirmed that LMM (Martlet) offers a mature, low-cost, high value solution to strengthen the inner layer defence capability of surface ships through re-use of current investment and the commonality and modularity between the helicopter and ship-based systems. Contact Thales Media Relations – Adrian Rondel, Media Relations, adrian.rondel@uk.thalesgroup.com, +44 (0)7971414052 View source version on Leonardo: https://www.leonardocompany.com/en/press-release-detail/-/detail/26-05-2020-leonardo-aw159-wildcat-helicopter-conducts-first-successful-firings-of-thales-martlet-lightweight-multirole-missile-lmm-

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  19 septembre 2018 | International, C4ISR

  Differentiating a port from a shipyard is a new kind of problem for AI

  By: Daniel Cebul It's well known that satellites and other intelligence, surveillance and reconnaissance platforms collect more data than is possible for humans to analyze. To tackle this problem, the Intelligence Advanced Research Projects Activity, or IARPA, conducted the Functional Map of the World (fMoW) TopCoder challenge from July 2017 through February 2018, inviting researchers in industry and academia to develop deep learning algorithms capable of scanning and identifying different classes of objects in satellite imagery. IARPA curated a dataset of 1 million annotated, high-resolution satellite images aggregated using automated algorithms and crowd sourced images for competitors to train their algorithms to classify objects into 63 classes, such as airports, schools, oil wells, shipyards, or ports. Researchers powered their deep learning algorithms by combining large neural networks, known as convolutional neural networks (CNNs), and computers with large amounts of processing power. The result was a network that, when fed massive amounts of training data, can learn to identify and classify various objects from satellite imagery. By combining a number of these networks into what is called an ensemble, the algorithm can judge the results from each CNN to produce a final, improved result that is more robust than any single CNN. This is how a team from Lockheed Martin, led by Mark Pritt, designed their deep learning algorithm for the challenge. Pritt explained to C4ISRNET, that he and his team developed their CNN using machine learning software and framework from online open source software libraries, such as Tensor Flow. Earning a top five finish, the algorithm designed by Pritt's team achieved a total accuracy of 83 percent, and was able to classify 100 objects per second. Pritt said that with fully functioning algorithm, this software could take an image recognition task that takes a human an hour to complete and reduce the process to a few seconds. The team's algorithm excelled at identifying classes with distinctive features, and successfully matched nuclear power plants, tunnel openings, runways, tool booths, and wind farms with accuracies greater than 95 percent, but struggled with more indiscreet classes such as shipyards and ports, hospitals, office buildings, and police stations. “Usually when you develop an algorithm its nice to see where it succeeds, but you actually learn the most where you look at where the algorithm fails or it doesn't do well,” Pritt said. In trying to decipher why the algorithms struggled, Pritt said the competitors suggested that some objects simply don't have any distinguishing features from the point of view of a satellite image for the algorithms to recognize. “Maybe the most important ingredient you need for these new types of algorithm to work is the dataset because these algorithms require a great amount of data to train on,” Pritt explained. “It's kind of analogous to the way a human will learn in childhood how to recognize things. You need lots of examples of what those things are and then you can start to generalize and make your own judgments,” he said. But even with large amounts of training data that is correctly labeled, it is also possible the deep learning technology of today cannot reach the higher levels of intelligence to recognize nuanced differences. For example, Lockheed Martin's algorithm confused shipyards and ports 56 percent of the time. Pritt said that people “look at an image and they can tell that it's a port or a shipyard, they are usually looking at very subtle things such as if there is a ship in dry dock or if there is a certain type of crane present. They are looking for details in the image that are maybe higher level or more complicated than what these deep learning algorithms can do right now.” However, the fact that these algorithms cannot do everything should not dismiss the significant contribution they could provide to the defense and intelligence community. Hakjae Kim, IARPA's program manager for the fMoW challenge, said the benefits of this technology could extend far beyond faster image processing. “I want to look at it more in the perspective that we can do things we weren't able to do before,” Kim said. “Because its technology that we are now able to do x, y and z, there are more applications you can create because with the human power it is just impossible to do before.” Kim and Pritt stressed managing expectations for CNN-based artificial intelligence. “This is a real technology that will work, but it also has limitations. I don't want to express this technology as a magic box that will just solve everything magically,” Kim said. “I don't want the users in the field to get disappointed by the initial delivery of this technology and say 'Oh, this is another technology that was oversold and this is not something we can use," he added. Part of managing our expectations for AI requires recognizing that although intelligence is in the name, this technology does not think and reason like humans. “A lot of the time we think that because we use the term AI, we tend to think these algorithms are like us, they are intelligent like us,” Pritt said. “And in someways they seem to mimic our intelligence, but when they fail we realize ‘Oh, this algorithm doesn't really know anything, [it] doesn't have any common sense.'” So how are IARPA and Lockheed Martin working to improve their algorithms? For IARPA, Kim's team is working on updating and maintaining their dataset to ensure algorithms have the most up to date information to train on, ultimately making the CNN-based algorithms easier to trust. “[S]ubtle changes in the area mess up the brains of the system and that system will give you a totally wrong answer,” Kim explained. “So we have planned to continuously look over the area and make sure the algorithm we are developing and reassessing for the government to test on and use to be robust enough for their application," he furthered. Work is also underway at American universities. Kim described how a team of researchers at Boston University are using the fMoW dataset and tested algorithms to create heat maps that visualize what part of the image algorithms are using to classify objects. They've found that sometimes it is not the object itself, but clues surrounding the object that aid most in classification. For example a “windmill that actually shows a shadow gives a really good indicator of what that object is,” Kim said. “Shadows show a better view of the object. A shadow is casting the side view of the object over on the ground, so [BU's heat map algorithm] actually points out the shadow is really important and the key feature to make the object identified as a windmill.” But don't expect these algorithms to take away the jobs of analysts any time soon. “I think you still need a human doing the important judgments and kind of higher level thinking,” Pritt said. “I don't think AI will take away our jobs and replace humans, but I think what we have to do is figure out how to use them as a tool and how to use them efficiently, and that of course requires understanding what they do well and what they do poorly," he concluded. https://www.c4isrnet.com/intel-geoint/2018/09/18/differentiating-a-port-from-a-shipyard-is-a-new-kind-of-problem-for-ai