Leonardo stake in Hensoldt could boost FCAS, Tempest commonalities

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  26 juin 2018 | International, C4ISR

  Team Dedrone Wins USSOCOM ‘Game of Drones’ Competition

  Dedrone announced that its team of Echodyne Corporation, Squarehead Technologies and Battelle, won first place at ThunderDrone's “Game of Drones” outdoor demonstration at Nellis Air Force Base and AFWERX enclave, June 18-20. Hosted by AFWERX, Team Dedrone bested five other teams in the last of three ThunderDrone rapid prototyping events focusing on countering small, unmanned aerial drones. Team Dedrone successfully demonstrated the capabilities of a layered detection, tracking, classification and mitigation solution that defends protected airspace against aerial drone threats. Initially 93 counter-drone technology companies formed teams and were narrowed down through a series of three rapid prototyping events. The Dedrone platform is a fully automatic counter-drone solution, designed to detect, classify and mitigate drone-based threats. Dedrone's software, DroneTracker, gathers intelligence from Dedrone's RF sensors, Echodyne's MESA radar and Squarehead Discovair acoustic sensor. Once DroneTracker makes a positive identification of a drone, Battelle's non-kinetic defense system, DroneDefender™, is automatically triggered to defeat the drone and eliminate the threat. The Dedrone platform combines hardware sensors and machine-learning software, providing early warning, classification of and mitigation against all drone threats. Based in San Francisco, Dedrone was founded in 2014. ThunderDrone is a U.S. Special Operations Command and SOFWERX initiative dedicated to drone prototyping, which focuses on exploring drone technologies through idea formation, testing and demonstrating efforts that are being conducted collaboratively with the Department of Defense's Strategic Capabilities Office. SOFWERX leads collaboration between special operations warfighters and select contributors from industry and academia on technology and innovation efforts to bring drones, tactical swarms and their associated data science applications to the special operations community. Team Dedrone Quotes: “Dedrone's open platform and architecture allows customers to combine the best in drone detection and mitigation technology, such as with RF sensors from Dedrone, radar from Echodyne, acoustic sensors from Squarehead, and jammers from Battelle,” shares Joerg Lamprecht, CEO and co-founder of Dedrone. “This multi-layered sensor platform ensures that all organizations, including those under USSOCOM, are provided complete airspace security that is safe from all drone threats.” “ThunderDrone provided us with our first opportunity to simultaneously engage against multiple UAVs with two DroneDefender V2 systems,” shares Alex Morrow, Technical Director of cUAS Programs at Battelle. “Battelle is pleased with another successful demonstration of our DroneDefender V2.” “Echodyne's compact solid-state radar repeatedly demonstrates an unparalleled combination of range, tracking accuracy and value in countering UAS threats,” notes Eben Frankenberg, Echodyne CEO. “We're pleased that our beam steering radar once again contributed its indispensable capabilities as part of the award-winning Team Dedrone solution.” “Discovair's directional acoustics add a near unspoofable layer of super hearing. The ability to deal with drones hinges on detection. In cluttered areas and up against any drone system, even passive ones – Discovair acoustic sensor has proven to shine,” shares Stig Nyvold, CEO of Squarehead Technology. “Discovair's importance as part in the systems of systems has once again been proved. We are very excited to be part of this team and look forward to the future.” https://www.uasvision.com/2018/06/26/team-dedrone-wins-ussocom-game-of-drones-competition/

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  29 mars 2019 | International, C4ISR, Autre défense

  DARPA: Using AI to Build Better Human-Machine Teams

  The inability of artificial intelligence (AI) to represent and model human partners is the single biggest challenge preventing effective human-machine teaming today. Current AI agents are able to respond to commands and follow through on instructions that are within their training, but are unable to understand intentions, expectations, emotions, and other aspects of social intelligence that are inherent to their human counterparts. This lack of understanding stymies efforts to create safe, efficient, and productive human-machine collaboration. “As humans, we are able to infer unobservable states, such as situational beliefs and goals, and use those to predict the subsequent actions, reactions, or needs of another individual,” said Dr. Joshua Elliott, a program manager in DARPA's Information Innovation Office (I2O). “Machines need to be able to do the same if we expect them to collaborate with us in a useful and effective way or serve as trusted members of a team.” Teaching machines social intelligence however is no small feat. Humans intuitively build mental models of the world around them that include approximations of the mental models of other humans – a skill called Theory of Mind (ToM). Humans use their ToM skill to infer the mental states of their teammates from observed actions and context, and are able to predict future actions based on those inferences. These models are built on each individual's existing sets of experiences, observations, and beliefs. Within a team setting, humans build shared mental models by aligning around key aspects of their environment, team, and strategies. ToM and shared mental models are key elements of human social intelligence that work together to enable effective human collaboration. DARPA's Artificial Social Intelligence for Successful Teams (ASIST) program seeks to develop foundational AI theory and systems that demonstrate the basic machine social skills necessary to facilitate effective machine-human collaboration. ASIST aims to create AI agents that demonstrate a Machine ToM, as well as the ability to participate effectively in a team by observing and understanding their environment and human partners, developing useful context-aware actions, and executing those actions at appropriate times. The agents developed under ASIST will need to operate across a number of scenarios, environments, and other variable circumstances, making the ability for them to evolve and adapt as needed critical. As such, ASIST will work to develop agents that can operate in increasingly complex environments, adapt to sudden change, and use observations to develop complex inferences and predictions. During the first phase of the program, ASIST plans to conduct experiments with single human-machine interactions to see how well the agents can infer human goals and situational awareness, using those insights to then predict their teammate's actions and provide useful recommended actions. As the program progresses, the complexity will increase with teams of up to 10 members interacting with the AI agents. During these experiments, ASIST will test the agents' ability to understand the cognitive model of the team – not just that of a single human – and use that understanding to develop appropriate situationally relevant actions. Full details on the program can be found in the Broad Agency Announcement (BAA) solicitation, which has been posted to the Federal Business Opportunities website, https://www.fbo.gov/index?s=opportunity&mode=form&id=9d4acf0aba98916288a541bd07810004&tab=core&_cview=1 https://www.darpa.mil/news-events/2019-03-21b

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  16 décembre 2021 | International, Aérospatial

  Les Émirats menacent d’annuler leur achat de chasseurs F-35 américains

  Les Émirats arabes unis ont menacé mardi d’annuler leur achat d’avions de combat F-35 auprès des États-Unis, en protestant contre des conditions jugées trop strictes, sur fond de méfiance américaine à l’égard de la Chine.