LATTE - Linking Acoustic Tests and Tagging Using Statistical Estimation

Abstract

We aim to construct and fit mathematical models of beaked whales diving behavior, and their response to MFA sonar. These models will be parameterized by fitting them simultaneously to three sources of data: (1) short-term, high fidelity tagging studies on individual whales (some of which comes from animals exposed to acoustic stimuli); (2) medium-term satellite tagging studies of individual whales (some of which we hope will come from data collected during navy exercises); and (3) long-term passive acoustic monitoring from bottom-mounted hydrophones (much of which comes from data collected during navy exercises). All data come from the Atlantic Undersea Test and Evaluation Center (AUTEC), Bahamas, and the surrounding area. Hence our models and predictions will be directly applicable to animals in that area, although we hope they will be of more general relevance. Outputs of the model are designed to be compatible with risk evaluation and mitigation tools and models developed under other ONR initiatives, such as Effects of Sound on the Marine Environment (ESME) and Population Consequences of Acoustic Disturbance (PCADS).

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2014
Accession Number
ADA616452

Entities

People

  • David Moretti
  • Ian L. Boyd
  • John Harwood
  • Len Thomas

Organizations

  • University of St Andrews

Tags

Communities of Interest

  • Sensors
  • Space

DTIC Thesaurus Topics

  • Acoustics
  • Algorithms
  • Animals
  • Computational Science
  • Data Processing
  • Detection
  • Hidden Markov Models
  • Marine Mammals
  • Markov Models
  • Mathematical Models
  • Models
  • Odontocetes
  • Probability
  • Random Walk
  • Simulations
  • Test And Evaluation
  • Three Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Marine Mammal Biology

Technology Areas

  • Space