Detection and Classification of Synthetic Aperture Radar Targets

Abstract

This final report described the ASSERT project 'Detection and Classification of Synthetic Aperture Radar Targets' associated with the URI Automatic Target Recognition (ATR) project sponsored by DARPA. The main goal of this ASSERT project together with the URI-ATR project is to develop detection and classification algorithms for automatic target recognition. For the ASSERT project, we have focused on the use of Bayesian probabilistic reasoning approach to fuse multiple target feature data for the purpose of target classification. We also developed Bayesian network learning algorithms to automatically construct the Bayesian network model. In this project, there were two graduate students and one undergraduate students participated in the technical work. Of whom, two of them have received M.S. degrees and one of them is continuing his Ph.D. degree. This project directly or indirectly supported the publications of eight technical papers, two Master thesis, one Ph.D. thesis, and one technical report.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 1997
Accession Number
ADA332580

Entities

People

  • Kai-Chi Chang

Organizations

  • George Mason University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Data Compression
  • Databases
  • Detection
  • Detectors
  • Information Science
  • Information Systems
  • Information Theory
  • Pattern Recognition
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Signal Processing
  • Synthetic Aperture Radar
  • Target Recognition

Readers

  • Neural Network Machine Learning.
  • Research Science/Academic Research
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML