Abstract
Decision fusion aims to intelligently combine decisions provided by a network of sensors. However, uncalibrated sensors or sensors of unknown reliability challenge this task because they significantly skew the fused decision. This work deals with decision fusion when no information on the sensor reliability is provided. To ensure high-performance fusion, side information is leveraged in the form of pairwise constraints, that capture relationships between pairs of data. A Bayesian approach is developed based on variational inference that can jointly assess sensor reliability, and perform label aggregation. Performance of the proposed algorithm is validated on real datasets.
| Original language | English (US) |
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| Title of host publication | 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 166-170 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781665428514 |
| DOIs | |
| State | Published - 2021 |
| Event | 22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021 - Lucca, Italy Duration: Sep 27 2021 → Sep 30 2021 |
Publication series
| Name | IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC |
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| Volume | 2021-September |
Conference
| Conference | 22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021 |
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| Country/Territory | Italy |
| City | Lucca |
| Period | 9/27/21 → 9/30/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Decision fusion
- classification
- constrained
- networks