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Title: Decentralized Detection with Conditionally Dependent Observations

Speaker: Kien C Nguyen

Abstract:

The paper deals with decentralized Bayesian detection with M hypotheses, and N sensors making conditionally correlated measurements regarding these hypotheses. Each sensor sends to a fusion center an integer from {0,1, .., D-1}, and the fusion center makes a decision on the actual hypothesis based on the messages it receives from the sensors so as to minimize the average probability of error. Such conditionally dependent scenarios arise in several applications of decentralized detection such as sensor networks and network security. Conditional dependence leads to a non-standard distributed decision problem where threshold based policies (on likelihood ratios) are no longer optimal, which results in a challenging distributed optimization/decision making problem. In this case, the minimum average probability of error cannot be expressed as a function of the marginal distributions of the sensor messages. Instead, we characterize this probability based on the joint distributions of these messages. We examine properties of the optimal decision rules at the sensors and the fusion rule at the fusion center.

Speaker Bio

Kien C. Nguyen received a B.Eng. in Electronics and Telecommunications from Hanoi University of Technology, Vietnam in 1997, an M.Eng.Sc. in Telecommunications from the University of New South Wales, Australia in 2002, and an M.S. in Electrical and Computer Engineering (ECE) from the University of Illinois at Urbana-Champaign (UIUC) in 2006. He is currently a Ph.D. Candidate in ECE at UIUC. His research interests include network security, hypothesis testing, and game theory.