Validation Test Report for a Genetic Algorithm in the Glider Observation STrategies (GOST 1.0) Project: Sensitivity Studies

Abstract

A suite of sensors in an oceanographic area of interest may be optimized with the use of a Genetic Algorithm (GA). The Environmental Measurements Path Planner (EMPath) executes a GA to generate optimal search plans for a suite of sensors based upon constituent cost-functions (CCF) contained in an input netcdf file. This GA software is incorporated to interface with the Relocatable Circulation Prediction System (RELO) under the Glider Observation Strategies (GOST) Project. This Validation Test Report (VTR) explores the sensitivity of a RELO to different Observation System Simulation Experiments (OSSEs) with simulated gliders in an area. Results are presented from a real-time exercise using the system, the Maritime Rapid Environmental Assessment of 2010 (MREA10), that include a full feedback cycle to guide a glider and assimilate the collected data back into the model.

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Document Details

Document Type
Technical Report
Publication Date
Aug 15, 2012
Accession Number
ADA569186

Entities

People

  • Charlie N. Barron
  • Emanuel Coelho
  • Germana Peggion
  • Kevin D. Heaney
  • Lucy F. Smedstad

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Acoustic Properties
  • Acoustics
  • Algorithms
  • Autonomous Underwater Vehicles
  • Case Studies
  • Department Of Defense
  • Environmental Assessment
  • Frequency
  • Genetic Algorithms
  • Graphical User Interface
  • Grids
  • Guidance
  • Kalman Filters
  • Measurement
  • Military Research
  • Ocean Currents
  • Underwater Acoustics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Database Systems and Applications
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

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