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Proceedings Paper

Minimum Variance SDF Design Using Adaptive Algorithms
Author(s): A. Mahalanobis
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Paper Abstract

The synthesis of minimum variance synthetic discriminant functions (MVSDF) for target detection in the presence of colored noise involves the invention of the noise correlation matrix. This may be numerically difficult even for low-resolution images. Methods must be found that allow synthesis of the MVSDF even when matrix inversion is not possible. We suggest an adaptive algorithm based on the well known LMS rule. Adaptive MVSDFs can maintain optimality in the presence of noise with unknown and non-stationary characteristics. The proposed adaptive synthesis technique "learns" characteristics of the noise source from sample realizations. This may seem as additional data and computation requirements, but comparable information and effort is necessary in the direct method to estimate the correlation matrix. The direct method (which uses the inverse of the correlation matrix) may be described as a two-step process, where the correlation matrix is determined in the first step and is used in the second step for the direct synthesis of the MVSDF. The proposed method iteratively converges on the optimum filter, and simultaneously gains knowledge about the noise source.

Paper Details

Date Published: 7 March 1989
PDF: 6 pages
Proc. SPIE 1005, Optics, Illumination, and Image Sensing for Machine Vision III, (7 March 1989); doi: 10.1117/12.949028
Show Author Affiliations
A. Mahalanobis, University of Arizona (United States)

Published in SPIE Proceedings Vol. 1005:
Optics, Illumination, and Image Sensing for Machine Vision III
Donald J. Svetkoff, Editor(s)

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