Pattern-Theoretic Foundations of Automatic Target Recognition in Clutter

Abstract

This effort advanced the art of applying Grenander's pattern theory to automatic target recognition (ATR) problems. We extended jump-diffusion ATR algorithms to accommodate unknown infrared camera calibration effects and include more numerically stable diffusion procedures for pose refinement, and developed flexible shape models to accommodate clutter. We also developed performance bounds on estimation and recognition performance for low-frequency radar data, single-image laser radar data, and 3-D "point cloud" data assembled from multiple sources. Further work explored data fusion using the "probability hypothesis density" approach.

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Document Details

Document Type
Technical Report
Publication Date
Nov 30, 2006
Accession Number
ADA469413

Entities

People

  • Aaron Lanterman

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Automated Target Recognition
  • Computer Vision
  • Data Fusion
  • Detection
  • Detectors
  • Image Processing
  • Information Processing
  • Information Science
  • Laser Radar
  • Multitarget Tracking
  • Point Clouds
  • Probability
  • Recognition
  • Target Recognition
  • Three Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.
  • Radar Systems Engineering.

Technology Areas

  • Directed Energy