Predicting Underwater Ambient Noise with an Evolutionary Signal Processing Method

Abstract

In this report we employ recent developments in non-linear physics and timeseries prediction to study the physical characteristics of measured underwater ambient sounds. Specifically, we examine the predictability of a sample of ocean ambient noise recorded in the Strait of Sicily, Italy. An approach based on genetic algorithms has been employed. Results indicate that, while showing complex time variability, the recorded signals are highly predictable

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2001
Accession Number
AD1113736

Entities

People

  • Alberto Alvarez
  • Chris Harrison
  • Martin Siderius

Organizations

  • SACLANT ASW Research Centre

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acoustic Signals
  • Algorithms
  • Ambient Noise
  • Artificial Intelligence
  • Band Structures
  • Coordinate Systems
  • Detection
  • Eigenvalues
  • Eigenvectors
  • Equations
  • Evolutionary Algorithms
  • Frequency
  • Genetic Algorithms
  • Nato
  • Neural Networks
  • Noise
  • Nonlinear Dynamics
  • Physics
  • Ship Noise
  • Signal Detection
  • Signal Processing
  • Time Domain

Readers

  • Acoustical Oceanography.
  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Oceanography.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology