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Proceedings Paper

Scenario Adaptive Midcourse Discrimination
Author(s): Keith Noren; Rick Dill; Steve Pitts
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Paper Abstract

This paper will describe a simple technique that can be used to generalize the target classification algorithms employed by passive midcourse sensors for strategic defense. Most discrimination algorithm evaluations have assumed a fixed engagement geometry (target location/orientation, sensor location, sun and earth angles). Pattern classifiers are trained and tested in that geometry and therefore are not fully applicable in a full scale engagement. By training on the full range of potential engagements, the important class signature dependencies can be stored in an expanded mean vector and covariance matrix . Then through standard statistical techniques, the mean and covariance can be properly conditioned to the geometry applicable to a particular track file. This paper demonstrates that this approach is capable of adapting discrimination algorithms to a general scenario without significant loss in classification accuracy.

Paper Details

Date Published: 30 June 1989
PDF: 7 pages
Proc. SPIE 1050, Infrared Systems and Components III, (30 June 1989); doi: 10.1117/12.951428
Show Author Affiliations
Keith Noren, SRS Technologies (United States)
Rick Dill, SRS Technologies (United States)
Steve Pitts, SRS Technologies (United States)

Published in SPIE Proceedings Vol. 1050:
Infrared Systems and Components III
Robert L. Caswell, Editor(s)

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