Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma

Mustafa Talha Bayram; Halim Sinan Balaban; Behçet Uğur Töreyin

doi:10.1109/SIU61531.2024.10600823

Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma

Translated title of the contribution: Deep Convolutional Neural Network Based Antenna Scan Type Classification using Power-Time Images

Mustafa Talha Bayram, Halim Sinan Balaban, Behçet Uğur Töreyin

Informatics Institute

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

Abstract

In Electronic Warfare (EW) systems, one of the most critical tasks is the accurate identification of radars in the environment. During the identification process, various uncertainties may arise. Antenna Scan Type (AST) serves as a significant parameter for resolving these ambiguities. In this study, a method based on Deep Convolutional Neural Networks (DCNN) with input as power-time images is proposed for the AST classification problem. Unlike previous studies in the literature, this method can operate independently of the number of pulses. The classification process was conducted using a DCNN-based model, and the results were shared with the readers Upon examination of the test results, it is observed that the proposed method yields successful outcomes for the AST classification problem.

Translated title of the contribution	Deep Convolutional Neural Network Based Antenna Scan Type Classification using Power-Time Images
Original language	Turkish
Title of host publication	32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350388961
DOIs	https://doi.org/10.1109/SIU61531.2024.10600823
Publication status	Published - 2024
Event	32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Mersin, Turkey Duration: 15 May 2024 → 18 May 2024

Publication series

Name	32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings

Conference

Conference	32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024
Country/Territory	Turkey
City	Mersin
Period	15/05/24 → 18/05/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Access to Document

10.1109/SIU61531.2024.10600823

Cite this

Bayram, M. T., Balaban, H. S., & Töreyin, B. U. (2024). Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma. In 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings (32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU61531.2024.10600823

Bayram, Mustafa Talha ; Balaban, Halim Sinan ; Töreyin, Behçet Uğur. / Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma. 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings).

@inproceedings{0485714541494ecaa8c01ebb1935746d,

title = "G{\"u}{\c c}-Zaman G{\"o}r{\"u}nt{\"u}lerini Kullanan Derin Evri{\c s}imsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma",

abstract = "In Electronic Warfare (EW) systems, one of the most critical tasks is the accurate identification of radars in the environment. During the identification process, various uncertainties may arise. Antenna Scan Type (AST) serves as a significant parameter for resolving these ambiguities. In this study, a method based on Deep Convolutional Neural Networks (DCNN) with input as power-time images is proposed for the AST classification problem. Unlike previous studies in the literature, this method can operate independently of the number of pulses. The classification process was conducted using a DCNN-based model, and the results were shared with the readers Upon examination of the test results, it is observed that the proposed method yields successful outcomes for the AST classification problem.",

keywords = "antenna scan type classification, cogintive electronic warfare, convolutional neural network, electronic warfare",

author = "Bayram, {Mustafa Talha} and Balaban, {Halim Sinan} and T{\"o}reyin, {Beh{\c c}et Uğur}",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 ; Conference date: 15-05-2024 Through 18-05-2024",

year = "2024",

doi = "10.1109/SIU61531.2024.10600823",

language = "T{\"u}rk{\c c}e",

series = "32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings",

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

booktitle = "32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings",

address = "United States",

}

Bayram, MT, Balaban, HS & Töreyin, BU 2024, Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma. in 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings. 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024, Mersin, Turkey, 15/05/24. https://doi.org/10.1109/SIU61531.2024.10600823

Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma. / Bayram, Mustafa Talha; Balaban, Halim Sinan; Töreyin, Behçet Uğur.
32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings).

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

TY - GEN

T1 - Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma

AU - Bayram, Mustafa Talha

AU - Balaban, Halim Sinan

AU - Töreyin, Behçet Uğur

PY - 2024

Y1 - 2024

N2 - In Electronic Warfare (EW) systems, one of the most critical tasks is the accurate identification of radars in the environment. During the identification process, various uncertainties may arise. Antenna Scan Type (AST) serves as a significant parameter for resolving these ambiguities. In this study, a method based on Deep Convolutional Neural Networks (DCNN) with input as power-time images is proposed for the AST classification problem. Unlike previous studies in the literature, this method can operate independently of the number of pulses. The classification process was conducted using a DCNN-based model, and the results were shared with the readers Upon examination of the test results, it is observed that the proposed method yields successful outcomes for the AST classification problem.

AB - In Electronic Warfare (EW) systems, one of the most critical tasks is the accurate identification of radars in the environment. During the identification process, various uncertainties may arise. Antenna Scan Type (AST) serves as a significant parameter for resolving these ambiguities. In this study, a method based on Deep Convolutional Neural Networks (DCNN) with input as power-time images is proposed for the AST classification problem. Unlike previous studies in the literature, this method can operate independently of the number of pulses. The classification process was conducted using a DCNN-based model, and the results were shared with the readers Upon examination of the test results, it is observed that the proposed method yields successful outcomes for the AST classification problem.

KW - antenna scan type classification

KW - cogintive electronic warfare

KW - convolutional neural network

KW - electronic warfare

UR - http://www.scopus.com/inward/record.url?scp=85200864196&partnerID=8YFLogxK

U2 - 10.1109/SIU61531.2024.10600823

DO - 10.1109/SIU61531.2024.10600823

M3 - Konferans katkısı

AN - SCOPUS:85200864196

T3 - 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings

BT - 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024

Y2 - 15 May 2024 through 18 May 2024

ER -

Bayram MT, Balaban HS, Töreyin BU. Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma. In 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2024. (32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings). doi: 10.1109/SIU61531.2024.10600823

Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this