Accelerating range Doppler imaging algorithm for multiple-receiver synthetic aperture sonar on multi-core-based architectures

  • Zhong HepingEmail author
  • Tang Jinsong
  • Tian Zhen
  • Wu Haoran
  • Ma Mengbo
Methodologies and Application


Synthetic aperture sonar (SAS) is an underwater high-resolution imaging method. But with the increase in resolution and mapping width, the amount of raw data used for imaging increases dramatically. To solve the problem of low imaging efficiency of SAS, an acceleration method of SAS imaging in shared memory environment is proposed. By analyzing the calculation characteristics of each step from the original data received to the synthetic aperture imaging result, the range compression, equivalent conversion from multi-receiver signal to single-receiver signal and azimuth compression are designed in parallel with OpenMP instructions, and the multi-core computing resources are fully utilized to accelerate the imaging process. Simulation experiment verifies the correctness of the parallel imaging algorithm. The experimental result of real data shows that the parallel imaging algorithm has high efficiency and can realize super real-time imaging. The efficiency of the proposed method can be changed with the number of computational kernels. The relationship between the acceleration ratio and the computational kernels is approximately linear, which improves the adaptability of the algorithm. Efficient synthetic aperture sonar imaging algorithm provides conditions for post-processing of image, such as image enhancement, image target detection and recognition.


Synthetic aperture sonar Range Doppler imaging algorithm Parallel computing Share memory 



This study was funded by the National Natural Science Foundation of China under Grant Nos. 61671461, 41304015, and by China Postdoctoral Science Foundation Grant No. 2015M582813.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Adams A, Lawlor M, Riyait V et al (1996) Real-time synthetic aperture sonar processing system. IEE Proc Radar Sonar Navig 143(3):169–176CrossRefGoogle Scholar
  2. Bellettini A, Pinto M (2009) Design and experimental results of a 300-kHz synthetic aperture sonar optimized for shallow-water operations. IEEE J Ocean Eng 34(3):285–293CrossRefGoogle Scholar
  3. Daniel PC, Daniel AC (2010) Using graphics processors to accelerate synthetic aperture sonar imaging via backpropagation. In: High Performance Embedded Computing Workshop, Burlington, pp. 1–3Google Scholar
  4. Groen J, Coiras E, Vera JDR et al (2010) Model-based sea mine classification with synthetic aperture sonar. IET Radar Sonar Navig 4(1):62–73CrossRefGoogle Scholar
  5. Hansen RE, Callow HJ, Sabo TO et al (2011) Challenges in seafloor imaging and mapping with synthetic aperture sonar. IEEE Trans Geosci Remote Sens 49(10):3677–3687CrossRefGoogle Scholar
  6. Hao G, Lim M, Ong Y et al (2019) Domination landscape in evolutionary algorithms and its applications. Soft Comput 23(11):3563–3570CrossRefGoogle Scholar
  7. Hayes MP, Gough PT (2009) Synthetic aperture sonar: a review of current status. IEEE J Ocean Eng 34(3):207–224CrossRefGoogle Scholar
  8. Jiang Z, Liu W, Li B et al (2011) A parallel processing method for high-frequency synthetic aperture sonar based on clusters. Appl Acoust 30(3):167–176Google Scholar
  9. Liu J, Li S, Li li et al (2003) Study on real-time and parallel implementation of synthetic aperture sonar signal processing. J Electron Inf Technol 25(6):777–783Google Scholar
  10. Myers V, Fawcett J (2010) A template matching procedure for automatic target recognition in synthetic aperture sonar imagery. IEEE Signal Process Lett 17(7):683–686CrossRefGoogle Scholar
  11. Ortiz J, Baralli F (2013) GPU-based real-time SAS processing on-board autonomous underwater vehicles. In: GPU technology conference, San Jose, pp 1–8Google Scholar
  12. Pasquale I, Antonio P, Riccardo L (2016) Spaceborne synthetic aperture radar data focusing on multicore-based architectures. IEEE Trans Geosci Remote Sens 54(8):4712–4731CrossRefGoogle Scholar
  13. Piper JE, Lim R, Thorsos EI et al (2009) Buried sphere detection using a synthetic aperture sonar. IEEE J Ocean Eng 34(4):485–494CrossRefGoogle Scholar
  14. Riyait VS, Lawlor MA, Adams AE et al (1995) Real-time synthetic aperture sonar imaging using a parallel architecture. IEEE J IP 4(7):1010–1019Google Scholar
  15. Thomas MB, Daniel PC, Daniel AC (2012) Using GPUs to accelerate synthetic aperture sonar imaging via backpropagation. In: GPU technology conference, San Jose, pp 1–21Google Scholar
  16. Tian Z, Tang J, Zhong H et al (2016) Extended range doppler algorithm for multiple-receiver synthetic aperture sonar based on exact analytical two-dimensional spectrum. IEEE J Ocean Eng 41(1):164–174CrossRefGoogle Scholar
  17. Yang H, Zhang S, Tang J (2011) Study on simulation of multiple-receiver synthetic aperture sonar imagery based on wide swath. J Syst Simul 23(7):1424–1428Google Scholar
  18. Zhang X, Tang J, Zhong H (2014) Multireceiver correction for the chirp scaling algorithm in synthetic aperture sonar’. IEEE J Ocean Eng 39(3):472–481CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Naval Institute of Underwater Acoustic TechnologyNaval University of EngineeringWuhanChina

Personalised recommendations