Using Machine Learning and Radar to Detect Drones in Complicated Urban Settings

As drones become more popular and more worrisome from a security standpoint, many projects have sought to engineer systems to spot them. During his time as a Defense Advanced Research Projects Agency (DARPA) program manager, Jeffrey Krolik, professor of electrical and computer engineering at Duke University, launched one such project called “Aerial Dragnet.” Using a network of drones hovering above a cityscape or other large, developed area in need of defense, multiple types of sensors would peer down into the city’s canyons and pick out any drones. The project has recently successfully concluded with an urban test in Rossyln, Virginia, but challenges remain in discriminating drones from urban “clutter.”

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