TY - GEN

T1 - Distributed hypothesis testing with a fusion center

T2 - 47th IEEE Conference on Decision and Control, CDC 2008

AU - Nguyen, Kien C.

AU - Alpcan, Tansu

AU - Başar, Tamer

PY - 2008

Y1 - 2008

N2 - 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. We show that, 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 also provide some numerical results for the case where the sensors' measurements follow bivariate normal distributions.

AB - 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. We show that, 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 also provide some numerical results for the case where the sensors' measurements follow bivariate normal distributions.

UR - http://www.scopus.com/inward/record.url?scp=62949244212&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=62949244212&partnerID=8YFLogxK

U2 - 10.1109/CDC.2008.4739150

DO - 10.1109/CDC.2008.4739150

M3 - Conference contribution

AN - SCOPUS:62949244212

SN - 9781424431243

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 4164

EP - 4169

BT - Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008

Y2 - 9 December 2008 through 11 December 2008

ER -