Asynchronous Data Fusion for AUV Navigation Using Extended Kalman Filtering

Abstract

A truly Autonomous Vehicle must be able to determine its global position in the absence of external transmitting devices. This requires the optimal integration of all available organic vehicle attitude and velocity sensors. This thesis investigates the extended Kalman filtering method to merge asynchronous heading, heading rate, velocity, and DGPS information to produce a single state vector. Different complexities of Kalman filters, with biases and currents, are investigated with data from Florida Atlantic's Ocean Explorer II surface run. This thesis used a simulated loss of DGPS data to represent the vehicle's submergence. All levels of complexity of the Kalman filters are shown to be much more accurate then the basic dead reckoning solution commonly used aboard autonomous underwater vehicles.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1997
Accession Number
ADA331863

Entities

People

  • Richard L. Thorne

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Autonomous Underwater Vehicles
  • Data Fusion
  • Dead Reckoning
  • Filters
  • Filtration
  • Global Positioning Systems
  • Grids
  • Inertial Navigation
  • Inertial Navigation Systems
  • Kalman Filtering
  • Kalman Filters
  • Measurement
  • Navigation
  • Seabed
  • Underwater Vehicles
  • Unmanned Underwater Vehicles
  • Vehicles

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Marine Hydrodynamics
  • Robotics and Automation.

Technology Areas

  • Autonomy