ABSTRACT
This chapter introduces the conventional design of decision rules at the local sensors and at the fusion center to optimize detection performance, under the Bayesian and Neyman–Pearson criteria. It discusses false discovery rate-based decision fusion which does not require the knowledge of the local sensor parameters while employing non-identical decision thresholds at each sensor. The chapter investigates the decision fusion problem, where the channels between the sensors and the fusion center are subject to fading and noise. It reviews channel aware decision fusion algorithms with different degrees of channel state information and focuses on fixed-sample-size detection problems for the parallel architecture. In fixed-sample-size detection, the fusion center arrives at a decision after receiving the entire set of sensor observations or decisions. For wireless sensor networks, the classical distributed detection framework needs to be reconsidered by taking into account the important features and limitations of sensors and the wireless channels between the sensors and the fusion center.
