Scenario Adaptive Midcourse Discrimination

Keith Noren; Rick Dill; Steve Pitts

doi:10.1117/12.951428

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30 June 1989 Scenario Adaptive Midcourse Discrimination

Keith Noren; Rick Dill; Steve Pitts

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Proceedings Volume 1050, Infrared Systems and Components III; (1989) https://doi.org/10.1117/12.951428
Event: OE/LASE '89, 1989, Los Angeles, CA, United States

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.

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Keith Noren, Rick Dill, and Steve Pitts "Scenario Adaptive Midcourse Discrimination", Proc. SPIE 1050, Infrared Systems and Components III, (30 June 1989); https://doi.org/10.1117/12.951428

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