Minimum information loss fusion in distributed sensor networks
| dc.contributor.author | Clarke, D | |
| dc.date.accessioned | 2016-09-20T15:20:25Z | |
| dc.date.available | 2016-09-20T15:20:25Z | |
| dc.date.issued | 2016-09-20 | |
| dc.description.abstract | A key assumption of distributed data fusion is that individual nodes have no knowledge of the global network topology and use only information which is available locally. This paper considers the weighted exponential product (WEP) rule as a methodology for conservatively fusing estimates with an unknown degree of correlation between them. We provide a preliminary investigation into how the methodology for selecting the mixing parameter can be used to minimize the information loss in the fused covariance as opposed to reducing the Shannon entropy, and hence maximize the information of the fused covariance. Our results suggest that selecting a mixing parameter which minimizes the information loss ensures that information which is exclusive to the estimates from one source is not lost during the fusion process. These results indicate that minimizing the information loss provides a robust technique for selecting the mixing parameter in WEP fusion. | en_UK |
| dc.identifier.citation | Clarke D (2016) Minimum information loss fusion in distributed sensor networks. In: 19th International Conference on Information Fusion, Heidelberg, 5-8 July 2016. | |
| dc.identifier.isbn | 978-0-9964-5274-8 | |
| dc.identifier.uri | https://ieeexplore.ieee.org/document/7528002 | |
| dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/10556 | |
| dc.rights | Attribution-NonCommercial 4.0 International | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.title | Minimum information loss fusion in distributed sensor networks | en_UK |
| dc.type | Conference paper | en_UK |
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