Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti

Sena Caybasi; Isin Erer

doi:10.1109/SIU66497.2025.11112194

Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti

Translated title of the contribution: Radar Target Detection using Improved Transformer Neural Network

Sena Caybasi^*, Isin Erer^*

^*Corresponding author for this work

Department of Electronics and Communication Engineering

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

Abstract

In this study, a method based on the improved Vision Transformer (ViT) architecture is proposed for radar target detection instead of traditional signal processing techniques. Instead of the conventional Multi-Layer Perceptron (MLP) structure, an advanced network architecture has been employed to enhance target detection performance in cluttered environments. The study utilizes both synthetic and real data. The proposed method has been compared with SO-CA CFAR, GO-CA CFAR, CA-CFAR, OS-CFAR, and CNN in terms of target detection accuracy. The results indicate that the proposed approach outperforms traditional CFAR methods and the deep learning-based CNN method, particularly in the presence of clutter.

Translated title of the contribution	Radar Target Detection using Improved Transformer Neural Network
Original language	Turkish
Title of host publication	33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331566555
DOIs	https://doi.org/10.1109/SIU66497.2025.11112194
Publication status	Published - 2025
Event	33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Istanbul, Turkey Duration: 25 Jun 2025 → 28 Jun 2025

Publication series

Name	33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings

Conference

Conference	33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025
Country/Territory	Turkey
City	Istanbul
Period	25/06/25 → 28/06/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Access to Document

10.1109/SIU66497.2025.11112194

Cite this

Caybasi, S., & Erer, I. (2025). Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti. In 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings (33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU66497.2025.11112194

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title = "Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti",

abstract = "In this study, a method based on the improved Vision Transformer (ViT) architecture is proposed for radar target detection instead of traditional signal processing techniques. Instead of the conventional Multi-Layer Perceptron (MLP) structure, an advanced network architecture has been employed to enhance target detection performance in cluttered environments. The study utilizes both synthetic and real data. The proposed method has been compared with SO-CA CFAR, GO-CA CFAR, CA-CFAR, OS-CFAR, and CNN in terms of target detection accuracy. The results indicate that the proposed approach outperforms traditional CFAR methods and the deep learning-based CNN method, particularly in the presence of clutter.",

keywords = "CFAR, CNN, Deep Learning, Radar, Target Detection, Transformer",

author = "Sena Caybasi and Isin Erer",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 ; Conference date: 25-06-2025 Through 28-06-2025",

year = "2025",

doi = "10.1109/SIU66497.2025.11112194",

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

series = "33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings",

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

booktitle = "33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings",

address = "United States",

}

Caybasi, S & Erer, I 2025, Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti. in 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings. 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, Istanbul, Turkey, 25/06/25. https://doi.org/10.1109/SIU66497.2025.11112194

Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti. / Caybasi, Sena; Erer, Isin.
33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. (33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings).

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

TY - GEN

T1 - Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti

AU - Caybasi, Sena

AU - Erer, Isin

PY - 2025

Y1 - 2025

N2 - In this study, a method based on the improved Vision Transformer (ViT) architecture is proposed for radar target detection instead of traditional signal processing techniques. Instead of the conventional Multi-Layer Perceptron (MLP) structure, an advanced network architecture has been employed to enhance target detection performance in cluttered environments. The study utilizes both synthetic and real data. The proposed method has been compared with SO-CA CFAR, GO-CA CFAR, CA-CFAR, OS-CFAR, and CNN in terms of target detection accuracy. The results indicate that the proposed approach outperforms traditional CFAR methods and the deep learning-based CNN method, particularly in the presence of clutter.

AB - In this study, a method based on the improved Vision Transformer (ViT) architecture is proposed for radar target detection instead of traditional signal processing techniques. Instead of the conventional Multi-Layer Perceptron (MLP) structure, an advanced network architecture has been employed to enhance target detection performance in cluttered environments. The study utilizes both synthetic and real data. The proposed method has been compared with SO-CA CFAR, GO-CA CFAR, CA-CFAR, OS-CFAR, and CNN in terms of target detection accuracy. The results indicate that the proposed approach outperforms traditional CFAR methods and the deep learning-based CNN method, particularly in the presence of clutter.

KW - CFAR

KW - CNN

KW - Deep Learning

KW - Radar

KW - Target Detection

KW - Transformer

UR - https://www.scopus.com/pages/publications/105015371686

U2 - 10.1109/SIU66497.2025.11112194

DO - 10.1109/SIU66497.2025.11112194

M3 - Konferans katkısı

AN - SCOPUS:105015371686

T3 - 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings

BT - 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025

Y2 - 25 June 2025 through 28 June 2025

ER -

Caybasi S, Erer I. Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti. In 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2025. (33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings). doi: 10.1109/SIU66497.2025.11112194

Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti

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