@inproceedings{760e8de2531b4e09847927e93f683a4e,
title = "Distributed decision fusion using the Neyman-Pearson criterion",
abstract = "Performance of a parallel binary decision fusion architecture is considered where both the local detectors and the Decision Fusion Center use the Neyman-Pearson criterion. The architecture comprises N local detectors in parallel, each sending a binary decision to a fusion center for integration. The algorithm designed here fixes the global false alarm rate and attempts to compute the local detector thresholds and the global fusion rule that achieve the maximum global detection probability. The key computational requirement is to find the roots of a certain Nth order polynomial. We compare the performance of our method with the performance of the Person-by-Person Optimization (PBPO) approach and that of a centralized detection scheme.",
keywords = "Decentralized Decision Fusion, Neyman-Pearson criterion, Optimal Detection, Person-by-Person Optimization",
author = "Sayandeep Acharya and Ji Wang and Moshe Kam",
note = "Publisher Copyright: {\textcopyright} 2014 International Society of Information Fusion.; 17th International Conference on Information Fusion, FUSION 2014 ; Conference date: 07-07-2014 Through 10-07-2014",
year = "2014",
month = oct,
day = "3",
language = "English (US)",
series = "FUSION 2014 - 17th International Conference on Information Fusion",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "FUSION 2014 - 17th International Conference on Information Fusion",
address = "United States",
}