题      目：Optimizing Decision Fusion with Budget Constraint

主  讲  人：陈惠民 博士 副教授 美国新奥尔良大学

时      间：2016年5月19日（周四）下午1:30

地      点：首师大北二区二层小会议室

主 办 单 位：必赢76net线路官网信息工程学院

主讲人介绍：

Huimin Chen received the B.E. and M.E. degrees from Department of Automation, Tsinghua University, Beijing, China, in 1996 and 1998, respectively, and the Ph.D. degree from the Department of Electrical and Computer Engineering, University of Connecticut, Storrs, in 2002, all in electrical engineering. He joined the Department of Electrical Engineering, University of New Orleans in Jan. 2003 and is currently a Don E. Wilson Chevron associate professor. His research interests are in general areas of signal processing, detection theory, and information fusion with applications to target recognition and target tracking.

内 容 介 绍：

We consider the problem of fusing local decision outputs into a global decision with a budget constraint. Each local decision maker is assumed to provide finite output regarding two competing hypotheses. A fusion rule is characterized by probabilistic mixing of decision trees corresponding to deterministic policies to reach a global decision. For practical problems where maximizing detection probability is of primary concern, we propose to optimize the fusion rule under the budget constraint via dynamic programming. The proposed algorithm can construct the complete efficient front of the detection probability vs. cost for practical decision fusion problems. Illustrative examples are provided for policy analysis within the optimization of decision fusion framework.