Space Object Identification by Filtered Fourier Transform Pattern Recognition Algorithm.

Abstract

Light intensity signals reflected from three classes of orbiting rocket bodies were analyzed using one-dimensional Fourier transforms. Low-frequency filtering in the transform domain and the Euclidean distance metric were used to classify the signals into the three classes. Using a portion of the data, linear decision boundaries were constructed by an adaptive training algorithm. It was found that the low-frequency filtered one-dimensional Fourier-transform domain gave good separation of the three classes of rocket bodies analyzed. A method of automated space object identification is proposed for non-stabilized satellites. It is suggested that the algorithm used in the study is also applicable to data collected via radar. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1973
Accession Number
AD0768347

Entities

People

  • William L. Malinski

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Adaptive Training
  • Algorithms
  • Artificial Satellites
  • Boundaries
  • Filtration
  • Frequency
  • Identification
  • Intensity
  • Pattern Recognition
  • Recognition
  • Space Objects
  • Training

Fields of Study

  • Engineering

Readers

  • Control Systems Engineering.
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Orbital Debris
  • Space - Space Objects