Learning the quality of sensor data in distributed decision fusion

Abstract- The problem of decision fusion has been studied for distributed sensor systems in the past two decades. Various techniques have been developed for either binary or multiple hypotheses decision fusion. However, most of them do not address the challenges that come with the changing quality of sensor data. In this paper we investigate adaptive decision fusion rules for multiple hypotheses within the framework of Dempster-Shafer theory. We provide a novel learn-ing algorithm for determining the quality of sensor data in the fusion process. In our approach each sen-sor actively learns the quality of information from different sensors and updates their reliabilities us-ing the weighted majority technique. Several exam-ples are provided to show the effectiveness of our approach