Broadband Signal Simulation in Shallow Water

Abstract

Today's minimum requirements for ocean acoustic models are to be able to simulate broadband signal transmissions in 2D varying environments with an acceptable computational effort. Standard approaches comprise ray, normal mode and parabolic equation techniques. In this report we compare the performance of four broadband models (GRAB, PROSIM, C-SNAP and RAM) on a set of shallow water test environments with propagation out to 10 km and a maximum signal bandwidth of 10-1000 Hz. It is shown that a computationally efficient modal approach as implemented in the PROSIM model is much faster than standard, less optimized models such as C-SNAP and RAM. However, the handling of range dependency in the adiabatic approximation is not always sufficiently accurate, and it is suggested that a mode coupling approach be adopted in PROSIM. Moreover, the interpolation of modal properties in range could lead to a further significant speed-up of mode calculations in range-dependent environments. It is concluded that coupled modes with wavenumber interpolation in both frequency and range remain the most promising wave modeling approach for broadband signal simulations in range-dependent shallow water environments. At higher frequencies (>1 kHz) there is currently no alternative to rays as a practical signal simulation tool.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2002
Accession Number
AD1113550

Entities

People

  • Carlo M. Ferla
  • Finn B. Jensen
  • Giovanna Martinelli
  • Peter L. Nielsen

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Acoustic Communications
  • Acoustic Fields
  • Acoustics
  • Algorithms
  • Bandwidth
  • Broadband
  • Communication Systems
  • Computations
  • Cross Correlation
  • Data Analysis
  • Equations
  • Frequency
  • Frequency Bands
  • Shallow Water
  • Transfer Functions
  • Wave Equations

Readers

  • Acoustical Oceanography.
  • Parallel and Distributed Computing.