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Learning the Quality of Sensor Data in Distributed Decision Fusion
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
doi:10.1109/icif.2006.301632
dblp:conf/fusion/YuS06
fatcat:wd7qkmp2xjdazol23ttu6tuhu4