Localization and tracking of RF emitting targets with multiple unmanned aerial vehicles in large scale environments with uncertain transmitter power

Mehmet Hasanzade, Omer Herekoglu, N. Kemal Ure, Emre Koyuncu, Ramazan Yeniceri, Gokhan Inalhan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)

Abstract

In this paper we study the localization and tracking of a radio frequency (RF) emitting target using multiple unmanned aerial vehicles (UAVs) over a large scale environment. Although localization of RF emitting targets using multiple measurements is a well studied problem, the standard approaches become inefficient when the signal power is uncertain and there is significant noise in the received signal strength (RSS) when the search environment is large scale. We present a localization and tracking architecture, where a data driven neural network model is used for estimating the unknown signal strength and extended Kalman filters are utilized for eliminating the RSS noise and increase the precision of target tracking performance. We present simulation results in a 10 × 10 km2 search area, where 3 fixed wing UAVs localize and track a target with up to 28.3 m average error distance.

Original languageEnglish
Title of host publication2017 International Conference on Unmanned Aircraft Systems, ICUAS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1058-1065
Number of pages8
ISBN (Electronic)9781509044948
DOIs
Publication statusPublished - 25 Jul 2017
Event2017 International Conference on Unmanned Aircraft Systems, ICUAS 2017 - Miami, United States
Duration: 13 Jun 201716 Jun 2017

Publication series

Name2017 International Conference on Unmanned Aircraft Systems, ICUAS 2017

Conference

Conference2017 International Conference on Unmanned Aircraft Systems, ICUAS 2017
Country/TerritoryUnited States
CityMiami
Period13/06/1716/06/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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