Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks

Abstract

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 detection performance. Our analysis reveals optimal beam tilt angles, and sensor density that maximize the network-wide detection of the drones.

Malicious UAV Detection Over Rician Fading Channel: Performance Analysis

“…Ambient RF signals emitted from the malicious UAV can be sensed at the terrestrial RF network under mixed channel propagation conditions. In [5], Sinha et al investigated the fundamental limits on the drone detection probability under mixed line of sight (LoS) and non-line of sight (NLoS) conditions using directional three-dimensional (3D) antenna patterns. They evaluated the detection probability numerically, which requires averaging over all possible locations of interfering sources and base stations using the Stable probability distribution.…”

“…Similar to the earlier works in[5],[11], we assume an acquisition of the UAV coordinates and channel exist for the probability of detection derivations.…”

A survey and analysis of drone detection systems using a systems approach superposed on scenarios

“…Sinha et al 44 discuss passive sensing of drones using existing terrestrial RF networks. An analytical framework is introduced by a network of ground RF sensors.…”

“…As in the case of the airport scenario, we have not come across papers that provide specific detection range experiments. Inspection of measures A_DDS_Entropy, A_DDS_Entropy_Index in subtables of scenario scoring sheets T10–T12 (Figure 9) and scoring sheets T10'–T12' (Figure 10) reveal that artifacts identified by (2), 36 (5), 39 (6), 40 (7), 41 (8), 42 (9), 43 (10), 44 (11), 45 (16), 50 and (21) 55 provide significant information covering most aspects.…”

Rethinking Dense Cells for Integrated Sensing and Communications: A Stochastic Geometric View

“…Tractable framework for symbol error probability, outage probability, ergodic rate, and throughput for downlink cellular networks with different MIMO configurations based on SG approach have been provided in [18]. In [19] analytical framework to study the joint impact of the sensor and the user equipment (UE) densities, on drones detection. In [20] a new mathematical approach that relies on a PPP model for the BSs locations was introduced to evaluate the performance of down-link MIMO cellular networks.…”