Detection of drones carries critical importance for
safely and effectively managing unmanned aerial system traffic
in the future. Given the ubiquitous presence of the drones across
all kinds of environments in the near future, wide area drone
detection and surveillance capability are highly desirable, which
require careful planning and design of drone sensing networks.
In this paper, we seek to meet this need by using the existing
terrestrial radio frequency (RF) networks for passive sensing
of drones. To this end we develop an analytical framework
that provides the fundamental limits on the network-wide drone
detection probability. In particular, we characterize the joint
impact of the salient features of the terrestrial RF networks, such
as the spatial randomness of the node locations, the directional
3D antenna patterns, and the mixed line of sight/non line of
sight (LoS/NLoS) propagation characteristics of the air-to-ground
(A2G) channels. Since the strength of the drone signal and the
aggregate interference in a sensing network are fundamentally
limited by the 3D network geometry and the inherent spatial
randomness, we use tools from stochastic geometry to derive the
closed-form expressions for the probabilities of detection, false
alarm and coverage. This, in turn, demonstrates the impact of the
sensor density, beam tilt angle, half power beam width (HPBW)
and different degrees of LoS dominance, on the projected detec?tion performance. Our analysis reveals optimal beam tilt angles,
and sensor density that maximize the network-wide detection of
the drones.