Identifying drone-related security risks by a laser vibrometer-based payload identification system

Abstract

Various drone detection systems (DDS) have been recently developed for civil and military applications. Such DDS are generally based on radio frequency (RF) radars, detecting control signals between drones and their pilots, drone's acoustic noise, optical surveillance, or a combination of these. However, existing DDS have safety critical gaps. For example, none of the current state-of-the-art technologies provide remote payload monitoring or verification. The registered payload of some commercial drones can be greatly increased by simple re-configuration procedures that may not be detected by current DDS. This study introduces patent-pending methods for remote identification and payload monitoring of standard and modified drones. Structural frequencies, measured by a long-range laser vibrometer, of commercial drones are proposed as a unique signature for remotely verifying registered specifications of a drone, e.g., payload capacity. In addition, a method is proposed to measure payload capacity of unknown drones based on their motion performance monitored via a motion dynamic model and a laser Doppler vibrometer. Preliminary flight tests have been successfully conducted for a group of standard and modified drones by the Institute of Flight Systems, DLR (German Aerospace Center)