Paper Info
Title
RSS-Based Detection of Drones in the Presence of RF Interferers
Abstract
Drones will have extensive use cases across various commercial, government, and military sectors, ranging from delivery of consumer goods to search and rescue operations. To maintain safety and security of people and infrastructure, it becomes critically important to quickly and accurately detect non-cooperating drones. In this paper we formulate a received signal strength (RSS) based detector, leveraging the existing wireless infrastructures that might already be serving other devices. Thus the detector should be able to detect the presence of a drone signal buried in radio frequency (RF) interference and thermal noise, in a mixed line-of-sight (LOS) and non-LOS (NLOS) environment. We develop analytical expressions for the probability of false alarm and the probability of detection of a drone, which quantify the impact of aggregate interference and air-to-ground (A2G) propagation characteristics on the detection performance of individual sensors. We also provide analytical expressions for the average network probability of detection, which captures the impact of sensor density on a network's detection coverage. Finally, we find the critical sensor density that maximizes the average network probability of detection for a given requirement of probability of false alarm.
Year
DOI
Venue
2020
10.1109/CCNC46108.2020.9045281
2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)
Keywords
Field
DocType
Unauthorized drone detection,LOS/NLOS,detection in non-Gaussian noise,PPP,stochastic geometry,UTM
Non-line-of-sight propagation,Wireless,False alarm,Computer science,Real-time computing,Ranging,Drone,Detector,Statistical power,RSS
Conference
ISSN
ISBN
Citations 
2331-9852
978-1-7281-3894-7
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Priyanka Sinha100.68
Yavuz Yapici23710.10
Ismail Güvenç32041153.03
esma turgut4162.32
Gursoy, M.Cenk5122.25