Optimal Wind Velocity Estimation.

Abstract

A new approach to remote wind velocity sensing is proposed, developed, and tested. Wind estimates were obtained from the spectral signature of an optical signal source. The signal was derived from the random scattering of a laser beam in the lower atmosphere. A quasi-supervised learning machine, using a minimal variance linear estimator, was used to produce the wind velocity estimate. New stopping rules, used to guarantee the convergence of the learning process, were also developed. Field experimentation indicates that the new system will perform well; even under extreme optical nonlinear conditions (severe turbulence).

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1975
Accession Number
ADA022055

Entities

People

  • Chao-huan Huang
  • Fredrick J. Taylor
  • Thomas H. Pries

Organizations

  • United States Army Communications-Electronics Command

Tags

DTIC Thesaurus Topics

  • Atmospheres
  • Convergence
  • Estimators
  • Guarantees
  • Laser Beams
  • Lasers
  • Learning
  • Learning Machines
  • Scattering
  • Supervised Machine Learning
  • Turbulence
  • Wind
  • Wind Velocity

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Fluid Dynamics.
  • Optical Physics and Photonics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Directed Energy