• Bennett, J. S., F. Stahr, and C. C. Eriksen, 2019: Determining Seaglider velocities automatically. University of Washington Tech. Rep., 50 pp., http://hdl.handle.net/1773/44948.

  • Eriksen, C. C., T. J. Osse, R. D. Light, T. Wen, T. W. Lehman, P. L. Sabin, J. W. Ballard, and A. M. Chiodi, 2001: Seaglider: A long-range autonomous underwater vehicle for oceanographic research. IEEE J. Oceanic Eng., 26, 424436, https://doi.org/10.1109/48.972073.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frajka-Williams, E., C. C. Eriksen, P. B. Rhines, and R. R. Harcourt, 2011: Determining vertical water velocities from Seaglider. J. Atmos. Oceanic Technol., 28, 16411656, https://doi.org/10.1175/2011JTECHO830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hubbard, R. M., 1980: Hydrodynamics technology for an advanced expendable mobile target (AEMT). University of Washington Applied Physics Laboratory Tech. Rep. APL-UW 8013, 34 pp.

    • Crossref
    • Export Citation
  • Merckelbach, L., D. Smeed, and G. Griffiths, 2010: Vertical velocities from underwater gliders. J. Atmos. Oceanic Technol., 27, 547563, https://doi.org/10.1175/2009JTECHO710.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merckelbach, L., A. Berger, G. Krahmann, M. Dengler, and J. R. Carpenter, 2019: A dynamic flight model for Slocum gliders and implications for turbulence microstructure measurements. J. Atmos. Oceanic Technol., 36, 281296, https://doi.org/10.1175/JTECH-D-18-0168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pelland, N. A., C. C. Eriksen, and M. F. Cronin, 2016: Seaglider surveys at Ocean Station Papa: Circulation and water mass properties in a meander of the North Pacific Current. J. Geophys. Res. Oceans, 121, 68166846, https://doi.org/10.1002/2016JC011920.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudnick, D. L., T. M. S. Johnston, and J. T. Sherman, 2013: High-frequency internal waves near the Luzon Strait observed by underwater gliders. J. Geophys. Res. Oceans, 118, 774784, https://doi.org/10.1002/jgrc.20083.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudnick, D. L., J. T. Sherman, and A. P. Wu, 2018: Depth-average velocity from Spray underwater gliders. J. Atmos. Oceanic Technol., 35, 16651673, https://doi.org/10.1175/JTECH-D-17-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snyder, W., L. Van Uffelen, and M. Renken, 2019: Effects of incorporating inertial measurements on the localization accuracy of the Seaglider AUV. OCEANS 2019, Marseille, France, IEEE, https://doi.org/10.1109/OCEANSE.2019.8867252.

    • Crossref
    • Export Citation
  • Todd, R., D. Rudnick, J. T. Sherman, W. Owens, and L. George, 2017: Absolute velocity estimates from autonomous underwater gliders equipped with Doppler current profilers. J. Atmos. Oceanic Technol., 34, 309333, https://doi.org/10.1175/JTECH-D-16-0156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Uffelen, L. J., and et al. , 2013: Estimating uncertainty in subsurface glider position using transmissions from fixed acoustic tomography sources. J. Acoust. Soc. Amer., 134, 32603271, https://doi.org/10.1121/1.4818841.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Uffelen, L. J., B. M. Howe, E.-M. Nosal, G. S. Carter, P. F. Worcester, and M. A. Dzieciuch, 2016: Localization and subsurface position error estimation of gliders using broadband acoustic signals at long range. IEEE J. Oceanic Eng., 41, 501508, https://doi.org/10.1109/JOE.2015.2479016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, S. E., C. M. Lee, and J. I. Gobat, 2014: Preliminary results in under-ice acoustic navigation for Seagliders in Davis Strait. 2014 Oceans, St. John’s, NL, Canada, IEEE, https://doi.org/10.1109/OCEANS.2014.7003070.

    • Crossref
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 100 100 44
Full Text Views 22 22 8
PDF Downloads 34 34 9

Assessing Seaglider Model-Based Position Accuracy on an Acoustic Tracking Range

View More View Less
  • 1 a School of Oceanography, University of Washington, Seattle, Washington
  • | 2 b Naval Undersea Warfare Center Keyport Division, Keyport, Washington
  • | 3 c Department of Ocean Engineering, University of Rhode Island, Narragansett, Rhode Island
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Seagliders are buoyancy-driven autonomous underwater vehicles whose subsurface position estimates are typically derived from velocities inferred using a flight model. We present a method for computing velocities and positions during the different phases typically encountered during a dive–climb profile based on a buoyancy-driven flight model. We compare these predictions to observations gathered from a Seaglider deployment on the acoustic tracking range in Dabob Bay (200 m depth, mean vehicle speeds ~30 cm s−1), permitting us to bound the position accuracy estimates and understand sources of various errors. We improve position accuracy estimates during long vehicle accelerations by numerically integrating the flight model’s fundamental momentum-balance equations. Overall, based on an automated estimation of flight-model parameters, we confirm previous work that predicted vehicle velocities in the dominant dive and climb phases are accurate to <1 cm s−1, which bounds the accumulated position error in time. However, in this energetic tidal basin, position error also accumulates due to unresolved depth-dependent flow superimposed upon an inferred depth-averaged current.

© 2021 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: James S. Bennett, jsb11@uw.edu

Abstract

Seagliders are buoyancy-driven autonomous underwater vehicles whose subsurface position estimates are typically derived from velocities inferred using a flight model. We present a method for computing velocities and positions during the different phases typically encountered during a dive–climb profile based on a buoyancy-driven flight model. We compare these predictions to observations gathered from a Seaglider deployment on the acoustic tracking range in Dabob Bay (200 m depth, mean vehicle speeds ~30 cm s−1), permitting us to bound the position accuracy estimates and understand sources of various errors. We improve position accuracy estimates during long vehicle accelerations by numerically integrating the flight model’s fundamental momentum-balance equations. Overall, based on an automated estimation of flight-model parameters, we confirm previous work that predicted vehicle velocities in the dominant dive and climb phases are accurate to <1 cm s−1, which bounds the accumulated position error in time. However, in this energetic tidal basin, position error also accumulates due to unresolved depth-dependent flow superimposed upon an inferred depth-averaged current.

© 2021 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: James S. Bennett, jsb11@uw.edu

Supplementary Materials

    • Supplemental Materials (ZIP 2.21 MB)
Save