Sensor Fusion Using Entropic measures of Dependence
As opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multivalued relations. This distinction is especially important when high fidelity models of the sensed phenomena do not exist. The properties of entropic measures are shown to fit...
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| Format: | Article |
| Language: | English |
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Universidad de Costa Rica
2011-07-01
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| Series: | Revista de Matemática: Teoría y Aplicaciones |
| Online Access: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/2099 |
| _version_ | 1797723198917181440 |
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| author | Paul B. Deignan |
| author_facet | Paul B. Deignan |
| author_sort | Paul B. Deignan |
| collection | DOAJ |
| description | As opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multivalued relations. This distinction is especially important when high fidelity models of the sensed phenomena do not exist. The properties of entropic measures are shown to fit within the Bayesian framework of hierarchical sensor fusion. A method of estimating probabilistic structure for categorical and continuous valued measurements that is unbiased for finite data collections is presented. Additionally, a branch and bound method for optimal sensor suite selection suitable for either target refinement or anomaly detection is described. Finally, the methodology is applied against a known data set used in a standard data mining competition that features both sparse categorical and ontinuous valued descriptors of a target. Excellent quantitative and computational results against this data set support the conclusion that the proposed methodology is promising for general purpose low level data fusion. |
| first_indexed | 2024-03-12T09:59:27Z |
| format | Article |
| id | doaj.art-83b040fcad284873a5f1fe42a0a7b11a |
| institution | Directory Open Access Journal |
| issn | 2215-3373 |
| language | English |
| last_indexed | 2024-03-12T09:59:27Z |
| publishDate | 2011-07-01 |
| publisher | Universidad de Costa Rica |
| record_format | Article |
| series | Revista de Matemática: Teoría y Aplicaciones |
| spelling | doaj.art-83b040fcad284873a5f1fe42a0a7b11a2023-09-02T11:51:39ZengUniversidad de Costa RicaRevista de Matemática: Teoría y Aplicaciones2215-33732011-07-0118229932410.15517/rmta.v18i2.20991997Sensor Fusion Using Entropic measures of DependencePaul B. Deignan0Integrated SystemAs opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multivalued relations. This distinction is especially important when high fidelity models of the sensed phenomena do not exist. The properties of entropic measures are shown to fit within the Bayesian framework of hierarchical sensor fusion. A method of estimating probabilistic structure for categorical and continuous valued measurements that is unbiased for finite data collections is presented. Additionally, a branch and bound method for optimal sensor suite selection suitable for either target refinement or anomaly detection is described. Finally, the methodology is applied against a known data set used in a standard data mining competition that features both sparse categorical and ontinuous valued descriptors of a target. Excellent quantitative and computational results against this data set support the conclusion that the proposed methodology is promising for general purpose low level data fusion.https://revistas.ucr.ac.cr/index.php/matematica/article/view/2099 |
| spellingShingle | Paul B. Deignan Sensor Fusion Using Entropic measures of Dependence Revista de Matemática: Teoría y Aplicaciones |
| title | Sensor Fusion Using Entropic measures of Dependence |
| title_full | Sensor Fusion Using Entropic measures of Dependence |
| title_fullStr | Sensor Fusion Using Entropic measures of Dependence |
| title_full_unstemmed | Sensor Fusion Using Entropic measures of Dependence |
| title_short | Sensor Fusion Using Entropic measures of Dependence |
| title_sort | sensor fusion using entropic measures of dependence |
| url | https://revistas.ucr.ac.cr/index.php/matematica/article/view/2099 |
| work_keys_str_mv | AT paulbdeignan sensorfusionusingentropicmeasuresofdependence |