Real-Time Wind Velocity Monitoring Based on Acoustic Tomography

  • Yong Bao
  • Jiabin JiaEmail author
Part of the Springer Natural Hazards book series (SPRINGERNAT)


Wind-related disasters cause tremendous loss around the world, therefore a fast, low-cost but accurate wind velocity monitoring technique is highly desirable and will provide great benefits for wind risk management. Acoustic travel-time tomography, which utilise the dependence of sound speed on the wind velocity along the sound propagation path, is considered to be a promising remote sensing technique for wind velocity monitoring. The success of acoustic tomography technique stems from then various advantage of non-invasive, low cost and easy to implement when compared to other techniques. This chapter describes the fundamentals of the simultaneously multi-channel time-of-flight measurements and the tomographic reconstruction of 2D horizontal wind velocity distributions based on the use of offline iteration method. The feasibility and effectiveness of the proposed methods will be numerically validated in a simulation study.


Acoustic tomography Wind velocity Remote sensing 


  1. 1.
    Marchigiani R et al (2013) Wind disasters: a comprehensive review of current management strategies. Int J Crit Illn Inj Sci 3(2):130–142CrossRefGoogle Scholar
  2. 2.
    Tamura Y (2009) Wind induced damage to buildings and disaster risk reduction. In: Proceedings of the APCWE-VII, Taipei, TaiwanGoogle Scholar
  3. 3.
    Mikkelsen T (2010) Remote sensing of wind. Remote sensing for wind energy, pp 7–20Google Scholar
  4. 4.
    Sheh R et al (2006) A low-cost, compact, lightweight 3d range sensor. In: Australian conference on robotics and automationGoogle Scholar
  5. 5.
    Spiesberger JL, Fristrup KM (1990) Passive localization of calling animals and sensing of their acoustic environment using acoustic tomography. Am Nat 135:107–153Google Scholar
  6. 6.
    Keith Wilson D, Thomson DW (1994) Acoustic tomographic monitoring of the atmospheric surface layer. J Atmos Oceanic Technol 11(3):751–769CrossRefGoogle Scholar
  7. 7.
    Vecherin SN et al (2007) Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion. J Acoust Soc Am 122(3):1416–1425CrossRefGoogle Scholar
  8. 8.
    Ziemann A, Arnold K, Raabe A (2002) Acoustic tomography as a remote sensing method to investigate the near-surface atmospheric boundary layer in comparison with in situ measurements. J Atmos Oceanic Technol 19(8):1208–1215CrossRefGoogle Scholar
  9. 9.
    Ziemann A, Arnold K, Raabe A (1998) Acoustic tomography in the atmospheric surface layer. In: Annales Geophysicae. Springer, BerlinGoogle Scholar
  10. 10.
    Ostashev V et al (2008) Recent progress in acoustic tomography of the atmosphere. In: IOP conference series: earth and environmental science. IOP PublishingGoogle Scholar
  11. 11.
    Holstein P et al (2004) Acoustic tomography on the basis of travel-time measurement. Meas Sci Technol 15(7):1420CrossRefGoogle Scholar
  12. 12.
    Jovanovic I (2008) Inverse problems in acoustic tomographyGoogle Scholar
  13. 13.
    Jovanovic I et al (2007) Efficient and stable acoustic tomography using sparse reconstruction methods. In: 19th international congress on acousticsGoogle Scholar
  14. 14.
    Dogan Z et al (2012) 3D reconstruction of wave-propagated point sources from boundary measurements using joint sparsity and finite rate of innovation. In: 2012 9th IEEE international symposium on biomedical imaging (ISBI). IEEEGoogle Scholar
  15. 15.
    Toši I et al (2010) Ultrasound tomography with learned dictionaries. In: 2010 IEEE international conference on acoustics, speech and signal processing. IEEEGoogle Scholar
  16. 16.
    Vecherin SN et al (2006) Time-dependent stochastic inversion in acoustic travel-time tomography of the atmosphere. J Acoust Soc Am 119(5):2579–2588CrossRefGoogle Scholar
  17. 17.
    Liu Y et al (2015) A method for simultaneous reconstruction of temperature and concentration distribution in gas mixtures based on acoustic tomography. Acoust Phys 61(5):597–605CrossRefGoogle Scholar
  18. 18.
    Kolouri S, Azimi-Sadjadi MR, Ziemann A (2014) Acoustic tomography of the atmosphere using unscented Kalman filter. IEEE Trans Geosci Remote Sens 52(4):2159–2171CrossRefGoogle Scholar
  19. 19.
    Ostashev VE, Wilson DK (2015) Acoustics in moving inhomogeneous media. CRC Press, Boca RatonGoogle Scholar
  20. 20.
    Li H, Takata S, Yamada A (2011) Tomographic measurement of vortex air flow field using multichannel transmission and reception of coded acoustic wave signals. Jpn J Appl Phys 50(7S):07HC09CrossRefGoogle Scholar
  21. 21.
    Norton SJ (1992) Unique tomographic reconstruction of vector fields using boundary data. IEEE Trans Image Process 1(3):406–412CrossRefGoogle Scholar
  22. 22.
    Jovanovic I, Sbaiz L, Vetterli M (2009) Acoustic tomography for scalar and vector fields: theory and application to temperature and wind estimation. J Atmos Oceanic Technol 26(8):1475–1492CrossRefGoogle Scholar
  23. 23.
    Gregor J, Fessler JA (2015) Comparison of SIRT and SQS for regularized weighted least squares image reconstruction. IEEE Trans Comput Imaging 1(1):44–55MathSciNetCrossRefGoogle Scholar
  24. 24.
    Liu S et al (2004) Prior-online iteration for image reconstruction with electrical capacitance tomography. IEE Proc Sci Meas Technol 151(3):195–200CrossRefGoogle Scholar
  25. 25.
    Kim BS et al (2015) Electrical resistance imaging of two-phase flow using direct Landweber method. Flow Meas Instrum 41:41–49CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Engineering, Institute for Digital CommunicationsThe University of EdinburghEdinburghUK

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