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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bin Yu Sycara, K. |
| Copyright Year | 2006 |
| Description | Author affiliation: Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA (Bin Yu; Sycara, K.) |
| 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 learning algorithm for determining the quality 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 |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 9527855 |
| Page Count | 8 |
| File Format | |
| ISBN | 1424409535 |
| DOI | 10.1109/ICIF.2006.301632 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-07-10 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | ISIF |
| Subject Keyword | Sensor fusion Sensor systems Computer science System testing Fuses Robot sensing systems Robot kinematics Fusion power generation Object detection Multiagent systems Dempster-Shafer theory distributed sensor systems decision fusion quality of information |
| Content Type | Text |
| Resource Type | Article |
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