Date of Award

4-11-2003

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Electrical and Computer Engineering

First Advisor

Steven C. Gustafson, PhD

Abstract

Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A metric distance metric is used that effectively identifies targets under real world conditions that include noise and obscuration.

AFIT Designator

AFIT-DS-ENG-03-04

DTIC Accession Number

ADA415121

Comments

Alternate title: Classification of Radar Targets Using Invariants

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