Abstract
In recent years, not only United States Armed Services but other Law-enforcement agencies have shown increasing
interest in employing drones for various surveillance and reconnaissance purposes. Further, recent advancements in
autonomous drone control and navigation technology have tremendously increased the geographic extent of dronebased
missions beyond the conventional line-of-sight coverage. Without any sophisticated requirement on data links
to control them remotely (human-in-loop), drones are proving to be a reliable and effective means of securing
personnel and soldiers operating in hostile environments. However, this autonomous breed of drones can potentially
prove to be a significant threat when acquired by antisocial groups who wish to target property and life in urban
settlements. To further escalate the issue, the standard detection techniques like RADARs, RF data link signature
scanners, etc..., prove futile as the drones are smaller in size to evade successful detection by a RADAR based
system in urban environment and being autonomous, have the capability of operating without a traceable active data
link (RF). Hence, towards investigating possible practical solutions for the issue, the research team at AFRL’s
Tec^Edge Labs under SATE and YATE programs has developed a highly scalable, geographically distributable and
easily deployable smartphone-based collaborative platform that can aid in detecting and tracking unidentified hostile
drones.
In its current state, this collaborative platform built on the paradigm of “Human-as-Sensors”, consists primarily of
an intelligent Smartphone application that leverages appropriate sensors on the device to capture a drone’s attributes
(flight direction, orientation, shape, color, etc..,) with real-time collaboration capabilities through a highly
composable sensor cloud and an intelligent processing module (based on a Probabilistic model) that can estimate
and predict the possible flight path of a hostile drone based on multiple (geographically distributed) observation data
points. This developed collaborative sensing platform has been field tested and proven to be effective in providing
real-time alerting mechanism for the personnel in the field to avert or subdue the potential damages caused by the
detected hostile drones.
