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Proceedings Paper

Fault detection and multiclassifier fusion for unmanned aerial vehicles (UAVs)
Author(s): Weizhong Yan
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Paper Abstract

UAVs demand more accurate fault accommodation for their mission manager and vehicle control system in order to achieve a reliability level that is comparable to that of a pilot aircraft. This paper attempts to apply multi-classifier fusion techniques to achieve the necessary performance of the fault detection function for the Lockheed Martin Skunk Works (LMSW) UAV Mission Manager. Three different classifiers that meet the design requirements of the fault detection of the UAAV are employed. The binary decision outputs from the classifiers are then aggregated using three different classifier fusion schemes, namely, majority vote, weighted majority vote, and Naieve Bayes combination. All of the three schemes are simple and need no retraining. The three fusion schemes (except the majority vote that gives an average performance of the three classifiers) show the classification performance that is better than or equal to that of the best individual. The unavoidable correlation between the classifiers with binary outputs is observed in this study. We conclude that it is the correlation between the classifiers that limits the fusion schemes to achieve an even better performance.

Paper Details

Date Published: 22 March 2001
PDF: 11 pages
Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); doi: 10.1117/12.421106
Show Author Affiliations
Weizhong Yan, GE Corporate Research and Development Ctr. (United States)

Published in SPIE Proceedings Vol. 4385:
Sensor Fusion: Architectures, Algorithms, and Applications V
Belur V. Dasarathy, Editor(s)

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