But if radar signals move down from the clouds and into a city's streets, there are suddenly many objects that can be mistaken for one another. With only distance, speed and direction to go on, drones can easily be "hidden in plain sight" on radar displays among slowly moving cars, bicyclists, a person jogging or even the spinning blades of an air conditioning unit.

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."

Using a fleet of friendly drones to find enemy drones makes sense in a setting for a military unit that is trying to secure a wide urban area. However, in settings where protection of a fixed asset such as an embassy, hospital or encampment is the goal, a system that can maintain a perimeter from a safe stand-off distance is required. Once again funded by DARPA, Krolik is turning to radar, machine learning and specialized hardware to make a drone surveillance system with sufficient range to allow drones to be detected and stopped before they reach a protected area in a city.