Scenario Adaptive Midcourse Discrimination

Keith Noren; Rick Dill; Steve Pitts

doi:10.1117/12.951428

30 June 1989 Scenario Adaptive Midcourse Discrimination

Keith Noren, Rick Dill, Steve Pitts

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.

Citation Download Citation

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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