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Proceedings Paper

Adaptive-model classifiers
Author(s): Todd McWhorter; Michael P. Clark
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Paper Abstract

In this paper we describe a classifier that updates its signature models as testing data arrive. This classification strategy has application to the train on synthetic data and test on measured data methodology prevalent in many ATR systems. Additionally, this type of classifier is applicable to situations where the fielded targets are variants of the targets on which the classifier was trained. The model adaptation is based on a robust estimator of the parameters in a linear subspace model. Like total least squares (TLS), this estimator allows for errors in both the data and in the subspace model. However, unlike total least squares, this estimator allows the perturbation of the model to be constrained. These constraints have simple geometric interpretations and allow for various levels of confidence in the a priori signal model. The estimators of this paper are also distinguished from TLS in that they are invariant to certain arbitrary scalings and rotations of the signal model. This property, which TLS does not possess, is shown to be essential for certain estimation and classification problems.

Paper Details

Date Published: 13 August 1999
PDF: 9 pages
Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357657
Show Author Affiliations
Todd McWhorter, Mission Research Corp. (United States)
Michael P. Clark, Mission Research Corp. (United States)

Published in SPIE Proceedings Vol. 3721:
Algorithms for Synthetic Aperture Radar Imagery VI
Edmund G. Zelnio, Editor(s)

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