RSS-Based Detection of Drones in the Presence of RF Interferers

Priyanka Sinha, Yavuz Yapici, Ismail Guvene, Esma Turgut, M. Cenk Gursoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728138930
DOIs
StatePublished - Jan 2020
Event17th IEEE Annual Consumer Communications and Networking Conference, CCNC 2020 - Las Vegas, United States
Duration: Jan 10 2020Jan 13 2020

Publication series

Name2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020

Conference

Conference17th IEEE Annual Consumer Communications and Networking Conference, CCNC 2020
CountryUnited States
CityLas Vegas
Period1/10/201/13/20

Keywords

  • detection in non-Gaussian noise
  • LOS/NLOS
  • PPP
  • stochastic geometry
  • Unauthorized drone detection
  • UTM

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Communication

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  • Cite this

    Sinha, P., Yapici, Y., Guvene, I., Turgut, E., & Cenk Gursoy, M. (2020). RSS-Based Detection of Drones in the Presence of RF Interferers. In 2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020 [9045281] (2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCNC46108.2020.9045281