Neural networks in the pursuit of invincible counterdrone systems

Abstract

The growing range of possibilities provided by the proliferation of commercial unmanned aerial vehicles, or drones, raises alarming safety and security threats. The efficient mitigation of these threats depends on authorities having defense systems to counter both accidentally trespassing and maliciously operated drones. To effectively counter such vehicles, defense systems must be able to detect a new drone entering a restricted airspace; locate its position; identify its purpose; and, should the identification procedure mark it as a threat, neutralize it.