An Acoustic Tomography Technique for Concurrently Observing the Structure of the Atmosphere and Water Bodies

Anthony Finn Defence and Systems Institute, University of South Australia, Mawson Lakes, South Australia, Australia

Search for other papers by Anthony Finn in
Current site
Google Scholar
PubMed
Close
and
Kevin Rogers Defence and Systems Institute, University of South Australia, Mawson Lakes, South Australia, Australia

Search for other papers by Kevin Rogers in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The opacity of water to radio waves means there are few, if any, techniques for remotely sensing it and the atmosphere concurrently. However, both these media are transparent to low-frequency sound (<300 Hz), which makes it possible to contemplate systems that take advantage of the natural integration along acoustic paths of signals propagating through both media. This paper proposes—and examines with theoretical analysis—a method that exploits the harmonics generated by the natural signature of a propeller-driven aircraft as it overflies an array of surface and underwater sensors. Correspondence of the projected and observed narrowband acoustic signals, which are monitored synchronously on board the aircraft and by both sensor sets, allows the exact travel time of detected rays to be related to a linear model of the constituent terms of sound speed. These observations may then be inverted using tomography to determine the inhomogeneous structures of both regions. As the signature of the aircraft comprises a series of harmonics between 50 Hz and 1 kHz, the horizontal detection limits of such a system may be up to a few hundred meters, depending on the depth of the sensors, roughness of the water surface, errors due to refraction, and magnitude of the sound field generated by the source aircraft. The approach would permit temperature, wind, and current velocity profiles to be observed both above and below the water’s surface.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Professor Anthony Finn, anthony.finn@unisa.edu.au; kevin.rogers@mymail.unisa.edu.au

Abstract

The opacity of water to radio waves means there are few, if any, techniques for remotely sensing it and the atmosphere concurrently. However, both these media are transparent to low-frequency sound (<300 Hz), which makes it possible to contemplate systems that take advantage of the natural integration along acoustic paths of signals propagating through both media. This paper proposes—and examines with theoretical analysis—a method that exploits the harmonics generated by the natural signature of a propeller-driven aircraft as it overflies an array of surface and underwater sensors. Correspondence of the projected and observed narrowband acoustic signals, which are monitored synchronously on board the aircraft and by both sensor sets, allows the exact travel time of detected rays to be related to a linear model of the constituent terms of sound speed. These observations may then be inverted using tomography to determine the inhomogeneous structures of both regions. As the signature of the aircraft comprises a series of harmonics between 50 Hz and 1 kHz, the horizontal detection limits of such a system may be up to a few hundred meters, depending on the depth of the sensors, roughness of the water surface, errors due to refraction, and magnitude of the sound field generated by the source aircraft. The approach would permit temperature, wind, and current velocity profiles to be observed both above and below the water’s surface.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Professor Anthony Finn, anthony.finn@unisa.edu.au; kevin.rogers@mymail.unisa.edu.au
Save
  • Arnold, K., A. Ziemann, and A. Raabe, 1999: Acoustic tomography inside the atmospheric boundary layer. Phys. Chem. Earth, 24B, 133137, doi:10.1016/S1464-1909(98)00024-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barth, M., and A. Raabe, 2011: Acoustic tomographic imaging of temperature and flow fields in air. J. Meas. Sci. Technol., 22, 035102, doi:10.1088/0957-0233/22/3/035102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braun, H., and A. Hauck, 1991: Tomographic reconstruction of vector fields. IEEE Trans. Signal Process., 39, 464471, doi:10.1109/78.80830.

  • Buckingham, M. J., and M. D. Richardson, 2002: On tone-burst measurements of sound speed and attenuation in sandy marine sediments. IEEE J. Oceanic Eng., 27, 429453, doi:10.1109/JOE.2002.1040929.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buckingham, M. J., and E. M. Giddens, 2004: A light aircraft as a low-frequency sound source for acoustical oceanography. Gayana (Concepción), 68 (2), 6970, doi:10.4067/S0717-65382004000200013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buckingham, M. J., E. M. Giddens, J. B. Pompa, F. Simonet, and T. R. Hahn, 2002a: Sound from a light aircraft for underwater acoustics experiments? Acta Acust. Acust., 88, 752755.

    • Search Google Scholar
    • Export Citation
  • Buckingham, M. J., E. M. Giddens, F. Simonet, and T. R. Hahn, 2002b: Propeller noise from a light aircraft for low-frequency measurements of the speed of sound in a marine sediment. J. Comput. Acoust., 10, 445, doi:10.1142/S0218396X02001760.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cornuelle, B. D., 1982: Acoustic tomography. IEEE Trans. Geosci. Remote Sensing, GE-20, 326332, doi:10.1109/TGRS.1982.350450.

  • Dunn, R. A., 2015: Ocean acoustic reverberation tomography. J. Acoust. Soc. Amer., 138, 34583469, doi:10.1121/1.4936857.

  • Dushaw, B. D., 2014: Ocean acoustic tomography. Encyclopedia of Remote Sensing, E. G. Njoku, Ed., Encyclopedia of Earth Sciences Series, Springer-Verlag, 4–10.

    • Crossref
    • Export Citation
  • Dushaw, B. D., and Coauthors, 2001: Observing the ocean in the 2000s: A strategy for the role of acoustic tomography in ocean climate observation. Observing the Oceans in the 21st Century: A Strategy for Global Ocean Observations, N. R. Smith and C. J. Koblinsky, Eds., GODAE Project Office, Bureau of Meteorology, 391–418.

  • Ferguson, B. G., 1996: Time-frequency signal analysis of hydrophone data. IEEE J. Oceanic Eng., 21, 537544, doi:10.1109/48.544063.

  • Ferguson, B. G., and K. W. Lo, 1999: Transiting aircraft parameter estimation using underwater acoustic sensor data. IEEE J. Oceanic Eng., 24, 424435, doi:10.1109/48.809262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferguson, B. G., and G. Speechley, 2009: Acoustic detection and localization of a turboprop aircraft by an array of hydrophones towed below the sea surface. IEEE J. Oceanic Eng., 34, 7582, doi:10.1109/JOE.2008.2011173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Finn, A., and S. Franklin, 2011a: Acoustic sense and avoid for UAV’s. 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE, 586–589, doi:10.1109/ISSNIP.2011.6146555.

    • Crossref
    • Export Citation
  • Finn, A., and S. Franklin, 2011b: UAV-based atmospheric tomography. Acoustics 2011: Breaking New Ground; Proceedings of the Annual Conference of the Australian Acoustical Society, D. J. Mee and I. D. M. Hillock, Eds., Australian Acoustical Society, P14. [Available online at http://www.acoustics.asn.au/conference_proceedings/AAS2011/papers/p14.pdf.]

  • Finn, A., and S. Franklin, 2012: Trials results for acoustic sense and avoid for UAVs. Defence and Systems Institute Rep. DA-AS-085-D0070, University of South Australia, 31 pp.

  • Finn, A., and K. Rogers, 2015: The feasibility of unmanned aerial vehicle-based acoustic atmospheric tomography. J. Acoust. Soc. Amer., 138, 874889, doi:10.1121/1.4926900.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Finn, A., and K. Rogers, 2016a: Acoustic atmospheric tomography using multiple unmanned aerial vehicles. IET Radar, Sonar Navig., 10, 15411551, doi:10.1049/iet-rsn.2016.0126.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Finn, A., and K. Rogers, 2016b: Improving unmanned aerial vehicle–based acoustic atmospheric tomography by varying the engine firing sequence of the aircraft. J. Atmos. Oceanic Technol., 33, 803816, doi:10.1175/JTECH-D-15-0170.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Franklin, S., and A. Finn, 2014: Acoustic sense and avoid (phase II): Real world validation of performance envelope—Final report. Sir Ross and Sir Keith Research Fund Rep. DASI-AF-2014-TR-6, Defence and Systems Institute, University of South Australia, 37 pp.

  • Golan, A., and V. Dose, 2002: Tomographic reconstruction from noisy data. AIP Conf. Proc., 617, 248258, doi:10.1063/1.1477051.

  • Hudimac, A., 1957: Ray theory solution for the sound intensity in water due to a point source above it. J. Acoust. Soc. Amer., 29, 916917, doi:10.1121/1.1909097.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jovanovic, I., L. Sbaiz, and M. Vetterli, 2009: Acoustic tomography for scalar and vector fields: Theory and application to temperature and wind estimation. J. Atmos. Oceanic Technol., 26, 14751492, doi:10.1175/2009JTECHA1266.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kolouri, S., and M. R. Azimi-Sadjadi, 2012: Acoustic tomography of the atmosphere using unscented Kalman filter. Proceedings of the 20th European Signal Processing Conference (EUSIPCO), IEEE, 25312535.

  • Kutakov, S. I., and I. A. Maslov, 2007: Water acoustic noises caused by air transport. Phys. Wave Phenom., 15, 201206, doi:10.3103/S1541308X07030077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lingjaerde, O. C., and N. Christopherson, 1998: Regularization principles: Solving ill-posed inverse problems. Department of Informatics, University of Oslo, 79 pp. [Available online at http://folk.uio.no/invpar/komp/kompendiumH98.ps.]

  • Lo, K. W., and B. G. Ferguson, 2004: Tactical unmanned aerial vehicle localization using ground-based acoustic sensors. Proceedings of 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, IEEE, 475480, doi:10.1109/ISSNIP.2004.1417507.

    • Crossref
    • Export Citation
  • Munk, W., and C. Wunsch, 1979: Ocean acoustic tomography: A scheme for large scale monitoring. Deep-Sea Res., 26A, 123161, doi:10.1016/0198-0149(79)90073-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Munk, W., P. Worcester, and C. Wunsch, 1995: Ocean Acoustic Tomography. Cambridge University Press, 120 pp.

    • Crossref
    • Export Citation
  • Ostashev, V., and K. D. Wilson, 2016: Acoustics in Moving Inhomogeneous Media. 2nd ed. CRC Press, 541 pp.

    • Crossref
    • Export Citation
  • Ostashev, V., A. Voronovich, and D. K. Wilson, 2000: Acoustic tomography of the atmosphere. IGARSS 2000: IEEE 2000 International Geoscience and Remote Sensing Symposium; Taking the Pulse of the Planet, T. I. Stein, Vol. 3, IEEE, 11861188, doi:10.1109/IGARSS.2000.858062.

    • Crossref
    • Export Citation
  • Ostashev, V., M. V. Scanlon, D. K. Wilson, and S. N. Vecherin, 2008a: Source localization from an elevated acoustic sensor array in a refractive atmosphere. J. Acoust. Soc. Amer., 124, 34133420, doi:10.1121/1.3003085.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ostashev, V., S. N. Vecherin, D. K. Wilson, A. Ziemann, and G. H. Goedecke, 2008b: Recent progress in acoustic tomography of the atmosphere. IOP Conf. Series: Earth Environ. Sci., 1, 012008, doi:10.1088/1755-1315/1/1/012008.

    • Crossref
    • Export Citation
  • Parkinson, B. W., and J. J. Spilker Jr., Ed., 1996: Global Positioning System: Theory and Applications, Volume X. Progress in Astronautics and Aeronautics, Vol. 163, AIAA, 793 pp.

  • Peng, Z.-H., Z.-L. Li, and G.-X. Wang, 2010: Matched bearing processing for airborne source localization by an underwater horizontal line array. Chin. Phys. Lett., 27, 114303, doi:10.1088/0256-307X/27/11/114303.

    • Search Google Scholar
    • Export Citation
  • Rice, F., and D. Gray, 2000: Bounds for aircraft tracking using a hydrophone array. Proceedings of 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, IEEE, 199–204, doi:10.1109/ISSNIP.2004.1417462.

    • Crossref
    • Export Citation
  • Rogers, K., and A. Finn, 2013a: 3D atmospheric tomography using UAVs. Proc. Acoustics 2013: Science, Technology and Amenity, Victor Harbour, SA, Australia, Australian Acoustical Society, 23. [Available online at https://acoustics.asn.au/conference_proceedings/AAS2013/papers/AAS2013-Victor_Harbor-abstracts.pdf.]

  • Rogers, K., and A. Finn, 2013b: Frequency estimation for 3D atmospheric tomography using unmanned aerial vehicles. IEEE ISSNIP: Sensing the Future; Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, M. Palaniswami et al., Eds., IEEE, 390395, doi:10.1109/ISSNIP.2013.6529822.

    • Crossref
    • Export Citation
  • Rogers, K., and A. Finn, 2013c: Three-dimensional UAV-based atmospheric tomography. J. Atmos. Oceanic Technol., 30, 336344, doi:10.1175/JTECH-D-12-00036.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, K., and A. Finn, 2016: Accuracy requirements for unmanned aerial vehicle-based acoustic atmospheric tomography. J. Acoust. Soc. Amer., 139, 2097, doi:10.1121/1.4950227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shmakov, S. L., 2011: A universal method of solving quartic equations. Int. J. Pure Appl. Math., 71, 251259.

  • Skarsoulis, E. C. B., 2004: Travel-time sensitivity kernels in ocean acoustic tomography. J. Acoust. Soc. Amer., 116, 227238, doi:10.1121/1.1753292.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snieder, R., and J. Trampert, 1999: Inverse problems in geophysics. Wavefield Inversion, A. Wirgin, Ed., International Centre for Mechanical Sciences, Vol. 398, Springer Verlag, 119–190, doi:10.1007/978-3-7091-2486-4_3.

    • Crossref
    • Export Citation
  • Spiesberger, J. L., and K. M. Fristrup, 1990: Passive localization of calling animals and sensing of their acoustic environment using acoustic tomography. Amer. Nat., 135, 107153, doi:10.1086/285035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sullivan, P. P., and E. G. Patton, 2011: The effect of mesh resolution on convective boundary layer statistics and structures generated by large-eddy simulation. J. Atmos. Sci., 68, 23952415, doi:10.1175/JAS-D-10-05010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tarantola, A., 2005: Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM, 339 pp., doi:10.1137/1.9780898717921.

    • Crossref
    • Export Citation
  • Urick, R., 1972: Noise signature of an aircraft in level flight over a hydrophone in the sea. J. Acoust. Soc. Amer., 52, 993999, doi:10.1121/1.1913206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecherin, S. N., V. E. Ostashev, G. H. Goedecke, D. K. Wilson, and A. G. Voronovich, 2006: Time-dependent stochastic inversion in acoustic travel-time tomography of the atmosphere. J. Acoust. Soc. Amer., 119, 25792588, doi:10.1121/1.2180535.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecherin, S. N., V. E. Ostashev, A. Ziemann, D. K. Wilson, K. Arnold, and M. Barth, 2007: Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion. J. Acoust. Soc. Amer., 122, 14161425, doi:10.1121/1.2756798.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecherin, S. N., V. E. Ostashev, and D. K. Wilson, 2008a: Three-dimensional acoustic travel-time tomography of the atmosphere. Acta Acust. Acust., 94, 349358, doi:10.3813/AAA.918042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecherin, S. N., V. E. Ostashev, D. K. Wilson, and A. Ziemann, 2008b: Time-dependent stochastic inversion in acoustic tomography of the atmosphere with reciprocal sound transmission. Meas. Sci. Technol., 19, 125501, doi:10.1088/0957-0233/19/12/125501.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiens, T., and P. Behrens, 2009: Turbulent flow sensing using acoustic tomography. 38th International Congress and Exposition on Noise Control Engineering 2009 (INTER-NOISE 2009), J. Bolton, C. Burroughs, and B. Gover, Eds., Vol. 5, INCE-USA, 3219–3227.

  • Wilson, D. K., and D. W. Thomson, 1994: Acoustic tomographic monitoring of the atmospheric surface layer. J. Atmos. Oceanic Technol., 11, 751769, doi:10.1175/1520-0426(1994)011<0751:ATMOTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, D. K., A. Ziemann, V. E. Ostashev, and A. Voronovich, 2001: An overview of acoustic travel-time tomography in the atmosphere and its potential applications. Acta Acust. Acust., 87, 721730.

    • Search Google Scholar
    • Export Citation
  • Wyber, R., 2014: The requirements of an acoustic aircraft collision avoidance system. Defence and Systems Institute Rep. DASI-RW-2014-TR-1, 65 pp.

  • Ziemann, A., K. Arnold, and A. Raabe, 1999: Acoustic travel time tomography—A method for remote sensing of the atmospheric surface layer. J. Meteor. Atmos. Phys., 71, 4351, doi:10.1007/s007030050042.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 373 139 11
PDF Downloads 211 42 10