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Kisitli l m ve Derin grenme Kullanarak Sentetik A iklikli Sonar G r nt leme

  • Ahmet Yigit Tabak*
  • , Isin Erer
  • *Bu çalışma için yazışmadan sorumlu yazar
  • ASELSAN Inc.
  • Istanbul Technical University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıHakem

Özet

Synthetic Aperture Sonar (SAS) is a high-resolution underwater imaging technique. Although SAS and Synthetic Aperture Radar (SAR) share similar imaging algorithms, differences in their propagation environments impose distinct constraints on these algorithms. Since the speed of sound in water is significantly slower than the speed of electromagnetic waves in air, the azimuth sampling rate of SAS is limited. In this study, in order to improve the azimuth sampling rate, additional spatial sampling points are generated using artificial intelligence methods and the received signal matrix is expanded along the azimuth axis. By applying the Range Migration Algorithm (RMA) to the expanded matrix, an enhanced SAS image was obtained. The proposed method has been trained and tested on simulation data. SAS images generated using the proposed method and those generated separately with a high azimuth sampling rate are compared using Peak Signal-to-Noise Ratio and Structural Similarity Index Measure.

Tercüme edilen katkı başlığıSynthetic Aperture Sonar Imagery Using Limited Measurements and Deep Learning
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9798331566555
DOI'lar
Yayın durumuYayınlandı - 2025
Etkinlik33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Istanbul, Türkiye
Süre: 25 Haz 202528 Haz 2025

Yayın serisi

Adı33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings

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???event.eventtypes.event.conference???33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025
Ülke/BölgeTürkiye
ŞehirIstanbul
Periyot25/06/2528/06/25

Bibliyografik not

Publisher Copyright:
© 2025 IEEE.

Keywords

  • data interpolation
  • deep learning
  • limited measurement data
  • range migration algorithm
  • synthetic aperture sonar

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