Learning the Quality of Sensor Data in Distributed Decision Fusion

Bin Yu, Katia Sycara
2006 2006 9th International Conference on Information Fusion  
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 learning algorithm for determining the
more » ... ty of sensor data in the fusion process. In our approach each sensor actively learns the quality of information from different sensors and updates their reliabilities using the weighted majority technique. Several examples are provided to show the effectiveness of our approach.
doi:10.1109/icif.2006.301632 dblp:conf/fusion/YuS06 fatcat:wd7qkmp2xjdazol23ttu6tuhu4