Evaluation of Ship Noise Levels Using a Single Vector Sensor on a Bottom-Mounted System

Abstract

This thesis demonstrates how a single bottom-mounted acoustic vector sensor can estimate distant-ship radiated noise levels by merging acoustic intensity data with automatic identification system (AIS) tracks and acoustic propagation modeling. A vector sensor measures pressure and three orthogonal directions of fluid motion. Our research involved a transportable bottom-mounted system with a suspended particle motion sensor. Research efforts were conducted along the shelf off the coast of Point Sur, California, to evaluate ship noise levels along the maritime shipping lane. Data collection combined the use of AIS, National Data Buoy Center wave data, self-measured sound speed profile, local wind information, and sensor data. Data was analyzed through use of MATLAB for comparison of AIS tracks to sensor data using intensity processing. Computational models include analysis of transmission loss through a shallow water propagation model called the Monterey-Miami Parabolic Equation (MMPE). Evaluation of transmission loss through MMPE and receive levels through MATLAB processing led to an estimation of source levels for selected ships.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2019
Accession Number
AD1080398

Entities

People

  • Steven Seda

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Acoustic Fields
  • Acoustic Propagation
  • Acoustic Waves
  • Acoustics
  • Automatic Identification Systems
  • Data Processing
  • Data Set
  • Detection
  • Detectors
  • Digital Data
  • Equations
  • Frequency
  • Frequency Bands
  • Global Positioning Systems
  • Identification Systems
  • Losses
  • Noise
  • Ocean Environments
  • Particles
  • Radiated Noise
  • Shallow Water
  • Ship Noise
  • Transmission Loss
  • Two Dimensional

Readers

  • Acoustical Oceanography.
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Oceanography.