The Agile Condor Pod provides on-board high-speed computer processing coupled with machine learning algorithms to detect, correlate, identify, and track targets of interest. With this capability, the MQ-9 is able to identify objects autonomously utilising its on-board Electro-optical/Infrared (EO/IR) sensor and GA-ASI’s Lynx Synthetic Aperture Radar (SAR).
High-powered computing at the edge enables autonomous target detection, identification and nomination at extended ranges and on-board processing reduces communication bandwidth requirements to share target information with other platforms. This is an important step towards greater automation, autonomous target detection, and rapid decision-making. GA-ASI will continue to work with AFRL to refine the capability and foster its transition to operational constructs that will improve warfighters’ ability to operate in contested or denied environments.
GA-ASI President David R. Alexander, said: “Computing at the edge has tremendous implications for future unmanned systems. GA-ASI is committed to expanding artificial intelligence capabilities on unmanned systems and the Agile Condor capability is proof positive that we can accurately and effectively shorten the observe, orient, decide and act cycle to achieve information superiority. GA-ASI is excited to continue working with AFRL to advance artificial intelligence technologies that will lead to increased autonomous mission capabilities.”
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