Abstract
In this letter, we investigate the asymptotic performance of categorical decision fusion in a human decision making framework. We assume that multiple human agents send categorized information to a moderator for final decision making. The local categorization is performed via a threshold based scheme where thresholds are assumed to be random variables. Considering the cases where the moderator has the knowledge of exact threshold values as well as when it has only probabilistic information of the individual thresholds, we analyze the asymptotic performance of likelihood ratio based decision fusion at the moderator in terms of the Chernoff information. Numerical results are presented for illustration.
Original language | English (US) |
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Article number | 6809962 |
Pages (from-to) | 994-997 |
Number of pages | 4 |
Journal | IEEE Signal Processing Letters |
Volume | 21 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2014 |
Keywords
- Asymptotic performance
- Chernoff information
- decision fusion
- human decision making
- likelihood ratio test
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics