Sequentially Distributed Detection and Data Fusion with Two Sensors

Abstract

The relationship of decision rule of sensor for each other is relevant to data fusion, so different topological networks of sensors usually results in different performances. This paper considers the sequential network fusion with two sensors in some detail and compares its performance with that of single detection and fusion. In this paper, the detection model is specified for binary hypotheses testing problem. In particular, this paper supposes that Bayesian risk cost of different decisions and the prior probability distribution of two hypotheses are known. Finally, this paper simulates the probabilities of error and Bayesian risk by these fusion rules with corresponding to different values of prior probabilities of two hypotheses by these fusion methods. And compared to single detection and fusion, the performance of sequential detection and fusion is better.