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30 June 1989 Scenario Adaptive Midcourse Discrimination
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Proceedings Volume 1050, Infrared Systems and Components III; (1989)
Event: OE/LASE '89, 1989, Los Angeles, CA, United States
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.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Keith Noren, Rick Dill, and Steve Pitts "Scenario Adaptive Midcourse Discrimination", Proc. SPIE 1050, Infrared Systems and Components III, (30 June 1989);

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