Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks

This chapter focuses on distributed detection and decision fusion problems, which involve the design of decision rules at the local sensors and at the fusion center to optimize detection performance, under either a Neyman-Pearson or a Bayesian criterion. It briefly introduces the fundamentals of detection theory. The chapter covers the conventional distributed detection problem with multiple sensors. It presents several important factors that affect the design of distributed detection algorithms. They are the topology of the sensor networks, relation between sensor observations (conditionally independent versus correlated), optimization criteria, and quantization levels. The chapter discusses the distributed detection in wireless sensor networks (WSNs). Assuming local sensor decision rules to be based on simple binary quantization of sensor observations, a novel method for fusion of correlated decisions using copula theory is discussed. Finally, the summary and some challenging issues for distributed detection are presented in the chapter.