M.N. Karavaev1, Y.A. Lebedev2, E.A. Panina3, L.E. Lavrentovich4
1–4 JSC Vector Research Institute (St. Petersburg, Russia)
1 kmn.2693@yandex.ru, 2 yuri.lebedev@me.com, 3 pan4itos21@gmail.com, 4 lavrentovich_le@nii-vektor.ru
Processing raw spaceborne synthetic aperture radars (SAR) data and synthesizing SAR images is the main operational task of SAR systems. To date, since the appearance of the first spaceborne SAR, a number of methods for synthesizing SAR images have been created. However, when solving the problems of constructing and system designing of SAR information processing complexes, the question arises of choosing a specific SAR image synthesis algorithm planned for implementation; also, this choice gets more complicated by the presence of a number of criteria that the software implementation of synthesis methods must satisfy. Thus, there is a need to conduct a comparative analysis of the existing SAR image synthesis methods, which is the basis of this work. So, the aims of the work are: to conduct a comparative analysis of the methods of synthesizing SAR images according to the formulated criteria; to develop methods for software and algorithmic implementation of these methods; to evaluate the hardware requirements imposed by algorithmic implementations of SAR image synthesis methods on the computing platform. The article considers a set of methods and algorithms for synthesizing radar images obtained from spaceborne SARs from the point of view of their software implementation on various software and hardware platforms. A detailed analysis of the results of the algorithms’ operation according to various criteria is provided; bottlenecks in computing facilities during the synthesis of SAR images are noted; a positive result is revealed when using optimized computing software libraries. Recommendations are given for constructing a hardware platform intended for the implementation of SAR image synthesis algorithms. The obtained results of the comparative analysis allow us to use them as a basis for designing software and hardware systems for processing SAR information in terms of selecting the most effective algorithm for synthesizing radar data.
Karavaev M.N., Lebedev Y.A., Panina E.A., Lavrentovich L.E. Comparative analysis of spaceborne synthetic aperture radar images synthesis methods for stripmap mode. Achievements of modern radioelectronics. 2025. V. 79. № 5. P. 36–44. DOI: https://doi.org/10.18127/ j20700784-202505-04 [in Russian]
- Verba V.S., Neronskij L.B., Osipov I.G., Turuk V.E`. Radiolokacionny`e sistemy` zemleobzora kosmicheskogo bazirovaniya / Pod red. V.S. Verby`. M.: Radiotexnika. 2010. 680 s.: il.
- Verba V.S., Neronskij L.B., Turuk V.E`. Perspektivny`e texnologii cifrovoj obrabotki radiolokacionnoj informacii kosmicheskix RSA. Monografiya / Pod red. V.S. Verby`. M.: Radiotexnika. 2019. 416 s. 0,5 p.l. czv.
- Ian G. Cummins, Frank H. Wong. Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation. Artech House, Boston | London, 2005.
- Drovosekova T.N. Analiz metodov i algoritmov fokusirovki izobrazhenij. E`lektronny`j sbornik trudov molody`x specialistov Poloczkogo gosudarstvennogo universiteta. Promy`shlennost`. (40). 30–33. https://journals.psu.by/specialists_industry/article/view/1720
- MATLAB Based SAR Signal Processor for Educational Use. Rinki Deo, Ankit Jamod, V. Deepika Rani Gopu and Y.S. Rao. https://www.csre.iitb.ac.in/~ysrao/ankit/igarss_2012_paper.pdf
- Yuan Y., Chen S., Zhang S., Zhao H. A Chirp Scaling Algorithm for Forward-Looking Linear-Array SAR with Constant Acceleration. IEEE Geosci. Remote Sens. Lett. 2018. 15. R. 88–91.
- Cumming I.G., Neo Y.L., Wong F.H. Interpretations of the Omega-K Algorithm and Comparisons with Other Algorithms. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS’03, Toulouse, July 21–25, 2003.
- Stolt R.H. Migration by Fourier Transform. Geophysics. 1978. 43. P. 23–48.
- Waller E.H., Keil A. & Friederich F. Quantum Range Migration Algorithm for Synthetic Aperture Radar Applications. Sci Rep 13, 11436 (2023). https://doi.org/10.1038/s41598-023-38611-x
- Conrad Sanderson, Ryan Curtin. Armadillo: A Template-Based C++ Library for Linear Algebra. Journal of Open-Source Software. 2016. V. 1. № 2. Р. 26.
- Conrad Sanderson and Ryan Curtin. Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimization. Mathematical and Computational Applications. 2019. V. 24. № 3.

