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Proceedings Paper

Relative performance of selected detectors
Author(s): Kenneth I. Ranney; Hiralal Khatri; Lam H. Nguyen; Jeffrey Sichina
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Paper Abstract

The quadratic polynomial detector (QPD) and the radial basis function (RBF) family of detectors -- including the Bayesian neural network (BNN) -- might well be considered workhorses within the field of automatic target detection (ATD). The QPD works reasonably well when the data is unimodal, and it also achieves the best possible performance if the underlying data follow a Gaussian distribution. The BNN, on the other hand, has been applied successfully in cases where the underlying data are assumed to follow a multimodal distribution. We compare the performance of a BNN detector and a QPD for various scenarios synthesized from a set of Gaussian probability density functions (pdfs). This data synthesis allows us to control parameters such as modality and correlation, which, in turn, enables us to create data sets that can probe the weaknesses of the detectors. We present results for different data scenarios and different detector architectures.

Paper Details

Date Published: 24 August 2000
PDF: 16 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396340
Show Author Affiliations
Kenneth I. Ranney, Army Research Lab. (United States)
Hiralal Khatri, Army Research Lab. (United States)
Lam H. Nguyen, Army Research Lab. (United States)
Jeffrey Sichina, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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