HRR TOS/TOM Features and Classifiers Using Boundary Methods

Abstract

This project is focused on the development of features and classifiers for TOS/TOM classifiers, specifically for HRR applications. The technical approach to this problem is broken into two parts: 1. feature generation and feature set evaluation, and 2. classifier design. Both data-driven and physics-based models to produce features are evaluated to determine which set(s) of features are robust to the differences between measured and synthetic data. Feature Set Evaluation (FSE) is accomplished using both conventional techniques (e.g., kNN) and using a technique called Boundary Methods. New classifier designs are being developed that use these features to construct classifiers and verify that the developed classifiers perform well when trained with synthetic data and tested on measured data. Moreover, the classifiers make efficient use of a limited set of stored templates in order to mitigate computational problems.

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Document Details

Document Type
Technical Report
Publication Date
Jun 15, 2000
Accession Number
ADA381678

Entities

People

  • Batu Ulug
  • Junshui Ma
  • Stanley C. Ahalt

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Boundaries
  • Classification
  • Communication Systems
  • Demographic Cohorts
  • Electrical Engineering
  • Electronic Mail
  • Engineering
  • Machine Learning
  • Pattern Recognition
  • Radar
  • Signal Processing
  • Supervised Machine Learning
  • Target Recognition
  • Template Patterns
  • Test And Evaluation

Fields of Study

  • Computer science

Readers

  • Computer Vision.