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Proceedings Paper

Beyond the adaptive matched filter: nonlinear detectors for weak signals in high-dimensional clutter
Author(s): James Theiler; Bernard R. Foy; Andrew M. Fraser
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Paper Abstract

For known signals that are linearly superimposed on gaussian backgrounds, the linear adaptive matched filter (AMF) is well-known to be the optimal detector. The AMF has furthermore proved to be remarkably effective in a broad range of circumstances where it is not optimal, and for which the optimal detector is not linear. In these cases, nonlinear detectors are theoretically superior, but direct estimation of nonlinear detectors in high-dimensional spaces often leads to flagrant overfitting and poor out-of-sample performance. Despite this difficulty in the general case, we will describe several situations in which nonlinearity can be effectively combined with the AMF to detect weak signals. This allows improvement over AMF performance while avoiding the full force of dimensionality's curse.

Paper Details

Date Published: 7 May 2007
PDF: 12 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656503 (7 May 2007); doi: 10.1117/12.719952
Show Author Affiliations
James Theiler, Los Alamos National Lab. (United States)
Bernard R. Foy, Los Alamos National Lab. (United States)
Andrew M. Fraser, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 6565:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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