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Journals / Türkiye Uzaktan Algılama Dergisi / 2019 / Cilt: 1 - Sayı: 1

Comparison of the synthetic aperture radar imaging algorithms

Yapay açıklıklı radar görüntüleme algoritmalarının karşılaştırılması

Enes YİĞİT
Pages
1–7
Publication Date
2019-12-15
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The obtained results provide comparative data on which technique is more useful for the desired purpose.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, the performances of the matched filter, back-projection and w-k algorithms used in the reconstruction of the synthetic aperture radar images are compared.
📊
Key Findings & Data:In the comparison, the parameters such as resolution, processing time, signal-to-noise ratio, signal-to-clutter ratio and integrated side lobe ratio are considered.
💡
Conclusion & Impact:The obtained results provide comparative data on which technique is more useful for the desired purpose.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Uzaktan Algılama · Yer-Bazlı Yapay Açıklıklı Radar · Uyumlanmış Filtre Algoritması

Abstract

In this study, the performances of the matched filter, back-projection and w-k algorithms used in the reconstruction of the synthetic aperture radar images are compared. In the comparison, the parameters such as resolution, processing time, signal-to-noise ratio, signal-to-clutter ratio and integrated side lobe ratio are considered. The reconstruction algorithms are applied to both simulation data and real ground-based synthetic aperture radar data measured in 14-18 GHz band. The obtained results provide comparative data on which technique is more useful for the desired purpose.

Özet

Bu çalışmada yapay açıklıklı radar görüntülerinin oluşturulmasında kullanılan yöntemlerden, Uyumlanmış Filtre, Geriye-İzdüşüm ve w-k algoritmalarının görüntü oluşturmadaki performansları karşılaştırılmıştır. Kıyaslamada çözünürlük, işlem süresi, sinyal-gürültü oranı, sinyal-karışıklık oranı ve tümleşik yan lop oranı gibi parametreler dikkate alınmıştır. Algoritmalar hem benzetim verilerine hem de 14- 18 GHz bandında ölçülen gerçek yer-bazlı yapay açıklıklı radar verilerine uygulanmıştır. Elde edilen sonuçlar, hangi tekniğin amaçlanan hedef için daha yararlı olduğu konusunda karşılaştırmalı veriler sağlamaktadır.

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Cite This Article

YİĞİT, E. (2019). Comparison of the synthetic aperture radar imaging algorithms. Türkiye Uzaktan Algılama Dergisi, Cilt: 1 - Sayı: 1, 1–7. https://ajindex.com/articles/813199

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References (13)

  1. A. Matthew T. (2008). A Detailed Look at the Omega-K Algorithm for Processing Synthetic Aperture Radar Data. A thesis Master of Science, Dep. of Elec. and Comp. Eng. Brigham Young University. Bennett, J. R. and Cumming I.G. (1979). A digital processor for the production of Seasat synthetic aperture radar imagery. Proc. SURGE Workshop, Frascati, ESA-SP-154. Cumming, I., Wong, F. and Raney, K. (1992). A SAR processing algorithm with no interpolation. Proc. IGARSS ’92, Houston, 376-379.
  2. Jin, M. Y. and C. Wu, “A SAR correlation algorithm which accommodates large range migration,” IEEE Trans. Geosci. Remote Sensing, vol. GE-22, pp. 592-597, 1984.
  3. Leva D., Nico G., Tarchi D., Fortuny-Guasch J., and Sieber A. J. (2003). Temporal analysis of a landslide by means of a ground-based SAR interferometer. IEEE Trans. Geosci. Remote Sensing, (41), 745–752.
  4. Nguyen M. P. (2011). Omega-K Algorithm – A Generalization for Highly Squinted Spotlight SAR Imaging with Dechirp-on-Receive. Asia-Pacific Conference on Synthetic Aperture Radar, Seoul, Korea, 137-140.
  5. Raney R.K., Runge H., Bamler R., Cumming I.G., Wong F.H. (1994). Precision SAR Processing Using ChirpScaling. IEEE Trans. Geosci. Remote Sensing, vol. 32(4), 786-799.
  6. Basu, S. and Bresler, Y. (2000). O(N2 log2N) filtered backprojection reconstruction algorithm for tomography. IEEE Transactions on Image Processing, 9(10), 1760-1773.
  7. Smith, A. M. (1991). A new approach to range-Doppler SAR processing. Int. J. Remote Sensing, vol. 12, 235-251.
  8. Yigit E., Demirci S., Yilmaz B., Ozdemir C. (2013b). Short range Ground based SAR Imaging Experiments Application of Back projection Algorithm. PIERS Proceedings, 872-875.
  9. Yigit E., Unal A., Demirci S., Vertiy A., Ozdemir C. (2011). An adapted matched filter algorithm for millimeter wave ground based squint mode SAR applications. IEEE, 2011
  10. International Conference on Infrared, Millimeter, and Terahertz Waves. Zhou Z. S., Boerner V., Sato M. (2004). Development of a Ground-Based Polarimetric Broadband SAR System for Non- Invasive Ground-truth Validation in Vegetation Monitoring. IEEE Trans. Geoscience and Remote Sensing. 42(9), 1803-1810.
  11. Barton D. K. (1988). Modern Radar System Analysis. Artech House, 1988
  12. Shu X., Munson, D. C., Basu, S. and Bresler, Y. (2000). An N2 log2N back-projection algorithm for SAR image formation. The Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, vol. 1, 3-7.
  13. Yigit E., Demirci Ş., Özdemir C., Tekbaş M. (2013a).