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Proceedings Paper

Fuzzy classification algorithm as applied to signal discrimination for navy theater-wide missile defense
Author(s): Craig O. Savage; Hai-Wen Chen; Jack G. Riddle; Harry A. Schmitt
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Paper Abstract

Given a set of training data and a feature extraction tool, fuzzy membership functions are created using regression analysis on the extracted features. These membership functions are then used to classify a signal into one of two basic classes (namely, threat or non-threat). Alternatively, the dat can be classified into M groups, as desired. For this paper, the training data form a set of modeled infrared intensities for subpixel objects, of the types expected for a prototypical ballistic missile defense engagement scenario. The feature extraction took used is a form of local discriminant bases, as described by Coifman and Saito4. The top N features (typically two to four) are then piped pairwise through a regression tool to determine if any statistically significant trends occur. If a trend is discovered, then a membership function is created for the relationship; otherwise, membership functions are created for each feature independently. An example of each is given. Results indicate great flexibility in managing misclassification of targets (Leakage) versus classifying a non-target as a target (False Alarms), depending on the choice of membership functions. Results for using seven extracted features on performance data show < 1% Leakage corresponding to 13% False Alarms.

Paper Details

Date Published: 13 October 2000
PDF: 12 pages
Proc. SPIE 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, (13 October 2000); doi: 10.1117/12.403619
Show Author Affiliations
Craig O. Savage, Raytheon Missile Systems (United States)
Hai-Wen Chen, Raytheon Missile Systems (United States)
Jack G. Riddle, Raytheon Missile Systems (United States)
Harry A. Schmitt, Raytheon Missile Systems (United States)

Published in SPIE Proceedings Vol. 4120:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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