Motion Prediction for Watercraft Operations and Recovery

Abstract

Simple probabilistic approach: Input roll, pitch, roll velocity, and pitch velocity past time history (or whatever relevant variables characterize the system) and nondimensionalize with each variables standard deviation. Search non-dimensional past time history for n neighbours nearest to the point of interest (point of interest being the time from which we wish to approximate forward, and n for this work was selected as 10). Note the actual dimensional roll, pitch, roll velocity and pitch velocity trajectories for the duration of interest immediately following each of the 10 nearest neighbours. Generate 1, 2, and 3 standard deviation (1, 2, 3) envelope curves of predicted motions based upon the mean value 1, 2, 3 at each time step from the neighbour time histories.

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Document Details

Document Type
Technical Report
Publication Date
Dec 08, 2010
Accession Number
ADA528162

Entities

People

  • Brook Sherman
  • Leigh Mccue
  • Zhiliang Xing

Organizations

  • Virginia Tech

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Data Sets
  • Differential Equations
  • Equations
  • Equations Of Motion
  • Information Operations
  • Military Research
  • Motivation
  • Neural Networks
  • Recovery
  • Sea Based
  • Ship Motion
  • Standards
  • Unmanned Vehicles
  • Vehicles
  • Watercraft

Readers

  • Computational Modeling and Simulation
  • Control Systems Engineering.