Transitive, Anti-Symmetric Relational Attributes in Structural Description Matching with Applications to Radar Target Identification

Abstract

A structural pattern recognition system for a radar target identification system is described. Segmentation of a radar measurement series is accomplished with a Prony-based estimation procedure. Matching of structural descriptions is developed for structural descriptions with specialized relational attributes and for which an inter-node-set distance function is available. The reformulated matching theory is applied to the radar target identification problem. The radar measurement process is described in detail. A parametric model for the measurement series is given. An estimation process for the parametric model is briefly described and the energy localization properties of the estimation process are made explicit. The effects of noise on a parametric decomposition are discussed. An alternative framework for the matching of structural descriptions is developed. This development includes a number of qualifications applied to the standard structural description framework. A required inter-node-set distance function is used to define an inter-structural-description distance. Qualifications applied to the relational portion of the structural description formalism lead to the use of registration parameters for resolution of relational discrepancies.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1990
Accession Number
ADA231318

Entities

People

  • F. D. Garber
  • O. S. Sands

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Cell Size
  • Classification
  • Computational Science
  • Computer Vision
  • Electrical Engineering
  • Frequency Domain
  • Geometry
  • Pattern Recognition
  • Probabilistic Models
  • Probability
  • Radar
  • Random Variables
  • Recognition
  • Signal Processing

Readers

  • Business Analytics
  • Computer Vision.
  • Structural Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference