Design and Implementation of Raw Data Compression System for Subsurface Detection SAR Based on FPGA


Synthetic aperture radar (SAR) has a wide range of applications in the field of remote sensing. Subject to the bandwidth and resource limitations of the onboard system, it is necessary to compress the raw data obtained from SAR. The traditional algorithms for the compression of SAR raw data result in loss of weak signals such as the essential subsurface signal in subsurface detection, leading to problems in practical applications. In order to solve the aforementioned problem, the present study proposes a novel SAR data compression algorithm (preserve weak signal based on block adaptive quantization) that aims to preserve the subsurface signals on the basis of the traditional block adaptive quantization algorithm and provides its corresponding field-programmable gate array implementation. The implementation of the system holds a certain application value with great advantages in the optimized resources and speed, the optional automatic working mode, and the strong scalability.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. Bicari D, Picardi G, Seu R (2002) Mars high resolution shallow radar (SHARAD) for the MRO 2005 mission. IEEE 2002

  2. Chané P, Plessis W, Warren P. Du P, Richard W. F (2019) Metrics to evaluate compression algorithms for raw SAR data. IET Radar, Sonar & Navigation. Received May 30, 2018. Revised Sep. 24, 2018.

  3. D L, P M, B, M B et al. (1995) Adaptive vector quantization for raw SAR data. IEEE.

  4. Dong F, Daoxiang AN, Huang X et al (2019) A phase calibration method based on phase gradient autofocus for airborne holographic SAR imaging. IEEE.

  5. Jianchao Fan, Deyi Wang, Jianhua Zhao et al. (2017) National sea area use dynamic monitoring based on GF–3 SAR imagery. Journal of Radars 6 (5)456–472

  6. Liao D (2019) Imaging of bunkers under slightly rough terrain with clutter-suppressed, subsurface interferometric SAR. IEEE.

  7. Lorenzo B (2011) Subsurface radar sounding of the Jovian Moon Ganymede. Proceedings of the IEEE.

  8. Ma T, Hu X, Wang J (2018) FPGA design and implementation of missile-borne SAR azimuth filtering. IEEE.

  9. Michal K, Adam K, Czesław L et al. (2017) Implementation of block adaptive quantizer as a peripheral module for the FPGA-based SAR system. IEEE 2017 18th International Radar Symposium (IRS).

  10. Peng LI, Liu X, Fang G (2018) Subsurface sounding radar data compression based on frame difference. Radar Science and Technology 16(5)

  11. Picardi G et al. (2003) "Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS): subsurface performances evaluation," 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695), Adelaide, SA, Australia, pp. 515-521

  12. Ronald K, William TKJ (1989) Block adaptive quantization of Magellan SAR Data-BAQ. IEEE.

  13. Lei Sheng, Taoye Zheng, Xujin Zhang (2008) Design and implementation of SAR raw data BAQ based On FPGA Asian & Pacific Conference on Synthetic Aperture. Jan. 4, 2008.

  14. Sujit B, Thomas B (2008) Synthetic aperture radar raw data encoding using compressed sensing. 2008 IEEE Radar Conference. doi:

  15. Zhijian Sun, Xuemei Liu, Zhongxing Ji (2008) The design of SAR signal processor’s data compress system in FPGA. International Symposium on Intelligent Information Technology Application Workshops. Jan. 21, 2008.

  16. Toshiaki A (2017) Compressed sensing for phase unwrapping of interferometric SAR data. 2017 17th International Conference on Control, Automation and Systems (ICCAS).

  17. L A. Varianytsia-Roshchupkina, SV. Roshchupin (2016) Subsurface object imaging with two types of RTR-differential GPR system. 2016 8th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS), 145 – 147

  18. PD Walker (2000) Subsurface permittivity estimation from ground-penetrating radar measurements. Record of the IEEE 2000 International Radar Conference. doi:

  19. Xiaoping LI, Enlai Huo, Mingyi Fan (2017) Mars subsurface detection radar signal analysis and processing. Radar Science and Technology15 (6). Revised Aug.1, 2017.

  20. Shichao Yao, Yanfei Wang, Bingchen Zhang et al. (2002). SAR raw data amplitude and phase compression algorithm. Journal of Electronics and Information Technology 24 (11).

Download references


This work is supported in part by the National Nature Science Foundation of China under Grants 61571345 and Yangtse Rive Scholar Bonus Schemes and Ten Thousand Talent Program.

Author information




All authors have participated in (a) conception and design, or analysis and interpretation of the data and (b) drafting the article or revising it critically for important intellectual content. All authors have participated in approval of the final version.

Corresponding author

Correspondence to Jing Zhang.

Ethics declarations

All authors have participated in approval of the final version.

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Sang, L., Li, X. et al. Design and Implementation of Raw Data Compression System for Subsurface Detection SAR Based on FPGA. J geovis spat anal 4, 2 (2020).

Download citation


  • SAR raw data
  • Compress
  • Subsurface detection
  • Weak signal
  • FPGA