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Proceedings Paper

Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF
Author(s): Julian A. Chernick; Leonid I. Perlovsky; David M. Tye
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Paper Abstract

This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution; on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

Paper Details

Date Published: 10 June 1994
PDF: 7 pages
Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); doi: 10.1117/12.177761
Show Author Affiliations
Julian A. Chernick, U.S. Army Materiel Systems Analysis Activity (United States)
Leonid I. Perlovsky, Nichols Research Corp. (United States)
David M. Tye, Nichols Research Corp. (United States)


Published in SPIE Proceedings Vol. 2232:
Signal Processing, Sensor Fusion, and Target Recognition III
Ivan Kadar; Vibeke Libby, Editor(s)

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