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Matched-field depth estimation for active sonar.

Granger Hickman1, Jeffrey L Krolik

  • 1Duke University, Department of Electrical and Computer Engineering, Box 90291, Durham, North Carolina 27708-0291, USA.

The Journal of the Acoustical Society of America
|March 6, 2004
PubMed
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This study introduces a matched-field depth estimation (MFDE) method for active sonar, enabling accurate underwater object localization. The technique effectively estimates scatterer depth in shallow waters using a horizontal receiver array and multipath propagation modeling.

Area of Science:

  • Ocean acoustics
  • Underwater acoustics
  • Signal processing

Background:

  • Estimating submerged object depth in shallow water is crucial for marine applications.
  • Active sonar with horizontal receiver arrays offers potential for localization.
  • Existing passive methods require complex modeling of multipath propagation.

Purpose of the Study:

  • To develop a novel matched-field depth estimation (MFDE) method for active sonar.
  • To enable accurate depth localization of submerged scatterers in shallow-water environments.
  • To overcome limitations of passive methods by not requiring knowledge of multipath amplitudes.

Main Methods:

  • Utilizing active sonar with a horizontal receiver array.
  • Employing numerical modeling of multipath propagation.

Related Experiment Videos

  • Developing a depth-dependent relative delay and elevation angle spread model.
  • Deriving a maximum likelihood depth estimate under uncorrelated ping returns and constant scatterer depth assumptions.

    • Main Results:

    • The MFDE method accurately estimates scatterer depth without needing complex multipath amplitude information.
    • Predicted depth estimation performance within 10% of water column depth at SNR > 10 dB.
    • Real data demonstrated depth estimation accuracy within 10 meters for an echo repeater.

    Conclusions:

    • The proposed MFDE method is effective for shallow-water active sonar depth estimation.
    • The technique offers improved performance and reduced complexity compared to passive methods.
    • Accurate depth localization is achievable even in challenging range-dependent environments.