RF Source Localization using Unmanned Aerial Vehicle with Particle Filter

Mehmet Hasanzade; Ömer Herekoǧlu; Ramazan Yeniçeri; Emre Koyuncu; Gökhan Inalhan

doi:10.1109/ICMAE.2018.8467555

RF Source Localization using Unmanned Aerial Vehicle with Particle Filter

Mehmet Hasanzade, Ömer Herekoǧlu, Ramazan Yeniçeri, Emre Koyuncu, Gökhan Inalhan

Department of Aeronautical Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

In this paper, we propose a solution for the localization problem of a radio frequency (RF) emitting source over a large scale environment with unmanned aerial vehicle (UAV). Target localization using received signal strength indicator(RSSI) is one of the most challenging problem because of noise characteristics. To evaluate the noise effect on RSSI, we perform a RSSI measurement test. This adds value for proper model and helps to implement a more realistic simulation system. For the localization process, the particle filter is utilized in this paper instead of tools such as Extended Kalman Filter with multi UAVs. Simulation environment and software-in-the-loop system are prepared to exhibit the conceptual proof with realistic models and autopilot system. Simulation results show that, mean search time for localization is 84.06 seconds and mean distance error is 13.96 meters.

Original language	English
Title of host publication	Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	284-289
Number of pages	6
ISBN (Electronic)	9781538672297
DOIs	https://doi.org/10.1109/ICMAE.2018.8467555
Publication status	Published - 18 Sept 2018
Event	9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018 - Budapest, Hungary Duration: 10 Jul 2018 → 13 Jul 2018

Publication series

Name	Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018

Conference

Conference	9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018
Country/Territory	Hungary
City	Budapest
Period	10/07/18 → 13/07/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

localization
particle filter
RF
RSSI
UAV

Access to Document

10.1109/ICMAE.2018.8467555

Cite this

Hasanzade, M., Herekoǧlu, Ö., Yeniçeri, R., Koyuncu, E., & Inalhan, G. (2018). RF Source Localization using Unmanned Aerial Vehicle with Particle Filter. In Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018 (pp. 284-289). Article 8467555 (Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMAE.2018.8467555

Hasanzade, Mehmet ; Herekoǧlu, Ömer ; Yeniçeri, Ramazan et al. / RF Source Localization using Unmanned Aerial Vehicle with Particle Filter. Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 284-289 (Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018).

@inproceedings{4d835ce677b54c16ac177a77c71baf6e,

title = "RF Source Localization using Unmanned Aerial Vehicle with Particle Filter",

abstract = "In this paper, we propose a solution for the localization problem of a radio frequency (RF) emitting source over a large scale environment with unmanned aerial vehicle (UAV). Target localization using received signal strength indicator(RSSI) is one of the most challenging problem because of noise characteristics. To evaluate the noise effect on RSSI, we perform a RSSI measurement test. This adds value for proper model and helps to implement a more realistic simulation system. For the localization process, the particle filter is utilized in this paper instead of tools such as Extended Kalman Filter with multi UAVs. Simulation environment and software-in-the-loop system are prepared to exhibit the conceptual proof with realistic models and autopilot system. Simulation results show that, mean search time for localization is 84.06 seconds and mean distance error is 13.96 meters.",

keywords = "localization, particle filter, RF, RSSI, UAV",

author = "Mehmet Hasanzade and {\"O}mer Herekoǧlu and Ramazan Yeni{\c c}eri and Emre Koyuncu and G{\"o}khan Inalhan",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018 ; Conference date: 10-07-2018 Through 13-07-2018",

year = "2018",

month = sep,

day = "18",

doi = "10.1109/ICMAE.2018.8467555",

language = "English",

series = "Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "284--289",

booktitle = "Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018",

address = "United States",

}

Hasanzade, M, Herekoǧlu, Ö, Yeniçeri, R , Koyuncu, E & Inalhan, G 2018, RF Source Localization using Unmanned Aerial Vehicle with Particle Filter. in Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018., 8467555, Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018, Institute of Electrical and Electronics Engineers Inc., pp. 284-289, 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018, Budapest, Hungary, 10/07/18. https://doi.org/10.1109/ICMAE.2018.8467555

RF Source Localization using Unmanned Aerial Vehicle with Particle Filter. / Hasanzade, Mehmet; Herekoǧlu, Ömer; Yeniçeri, Ramazan et al.
Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 284-289 8467555 (Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - RF Source Localization using Unmanned Aerial Vehicle with Particle Filter

AU - Hasanzade, Mehmet

AU - Herekoǧlu, Ömer

AU - Yeniçeri, Ramazan

AU - Koyuncu, Emre

AU - Inalhan, Gökhan

PY - 2018/9/18

Y1 - 2018/9/18

N2 - In this paper, we propose a solution for the localization problem of a radio frequency (RF) emitting source over a large scale environment with unmanned aerial vehicle (UAV). Target localization using received signal strength indicator(RSSI) is one of the most challenging problem because of noise characteristics. To evaluate the noise effect on RSSI, we perform a RSSI measurement test. This adds value for proper model and helps to implement a more realistic simulation system. For the localization process, the particle filter is utilized in this paper instead of tools such as Extended Kalman Filter with multi UAVs. Simulation environment and software-in-the-loop system are prepared to exhibit the conceptual proof with realistic models and autopilot system. Simulation results show that, mean search time for localization is 84.06 seconds and mean distance error is 13.96 meters.

AB - In this paper, we propose a solution for the localization problem of a radio frequency (RF) emitting source over a large scale environment with unmanned aerial vehicle (UAV). Target localization using received signal strength indicator(RSSI) is one of the most challenging problem because of noise characteristics. To evaluate the noise effect on RSSI, we perform a RSSI measurement test. This adds value for proper model and helps to implement a more realistic simulation system. For the localization process, the particle filter is utilized in this paper instead of tools such as Extended Kalman Filter with multi UAVs. Simulation environment and software-in-the-loop system are prepared to exhibit the conceptual proof with realistic models and autopilot system. Simulation results show that, mean search time for localization is 84.06 seconds and mean distance error is 13.96 meters.

KW - localization

KW - particle filter

KW - RF

KW - RSSI

KW - UAV

UR - http://www.scopus.com/inward/record.url?scp=85055529469&partnerID=8YFLogxK

U2 - 10.1109/ICMAE.2018.8467555

DO - 10.1109/ICMAE.2018.8467555

M3 - Conference contribution

AN - SCOPUS:85055529469

T3 - Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018

SP - 284

EP - 289

BT - Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018

Y2 - 10 July 2018 through 13 July 2018

ER -

Hasanzade M, Herekoǧlu Ö, Yeniçeri R , Koyuncu E , Inalhan G. RF Source Localization using Unmanned Aerial Vehicle with Particle Filter. In Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 284-289. 8467555. (Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018). doi: 10.1109/ICMAE.2018.8467555

RF Source Localization using Unmanned Aerial Vehicle with Particle Filter

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this