A Bayesian approach to optimal sensor placement
<p>By "intelligently" locating a sensor with respect to its environment it is possible to minimize the number of sensing operations required to perform many tasks. This is particularly important for sensing media which provide only "sparse" data, such as tactile sensors an...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
1989
|
| Subjects: |
| _version_ | 1826290774322446336 |
|---|---|
| author | Cameron, A A. J. Cameron |
| author2 | Durrant-Whyte, H |
| author_facet | Durrant-Whyte, H Cameron, A A. J. Cameron |
| author_sort | Cameron, A |
| collection | OXFORD |
| description | <p>By "intelligently" locating a sensor with respect to its environment it is possible to minimize the number of sensing operations required to perform many tasks. This is particularly important for sensing media which provide only "sparse" data, such as tactile sensors and sonar. In this thesis, a system is described which uses the principles of statistical decision theory to determine the optimal sensing locations to perform recognition and localization operations. The system uses a Bayesian approach to utilize any prior object information (including object models or previously-acquired sensory data) in choosing the sensing locations.</p> |
| first_indexed | 2024-03-07T02:49:20Z |
| format | Thesis |
| id | oxford-uuid:ad201132-d418-4ee4-a9d5-3d79bd4876a7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:49:20Z |
| publishDate | 1989 |
| record_format | dspace |
| spelling | oxford-uuid:ad201132-d418-4ee4-a9d5-3d79bd4876a72022-03-27T03:33:28ZA Bayesian approach to optimal sensor placementThesishttp://purl.org/coar/resource_type/c_db06uuid:ad201132-d418-4ee4-a9d5-3d79bd4876a7Optical fiber detectorsEnglishPolonsky Theses Digitisation Project1989Cameron, AA. J. CameronDurrant-Whyte, HDurrant-Whyte, H<p>By "intelligently" locating a sensor with respect to its environment it is possible to minimize the number of sensing operations required to perform many tasks. This is particularly important for sensing media which provide only "sparse" data, such as tactile sensors and sonar. In this thesis, a system is described which uses the principles of statistical decision theory to determine the optimal sensing locations to perform recognition and localization operations. The system uses a Bayesian approach to utilize any prior object information (including object models or previously-acquired sensory data) in choosing the sensing locations.</p> |
| spellingShingle | Optical fiber detectors Cameron, A A. J. Cameron A Bayesian approach to optimal sensor placement |
| title | A Bayesian approach to optimal sensor placement |
| title_full | A Bayesian approach to optimal sensor placement |
| title_fullStr | A Bayesian approach to optimal sensor placement |
| title_full_unstemmed | A Bayesian approach to optimal sensor placement |
| title_short | A Bayesian approach to optimal sensor placement |
| title_sort | bayesian approach to optimal sensor placement |
| topic | Optical fiber detectors |
| work_keys_str_mv | AT camerona abayesianapproachtooptimalsensorplacement AT ajcameron abayesianapproachtooptimalsensorplacement AT camerona bayesianapproachtooptimalsensorplacement AT ajcameron bayesianapproachtooptimalsensorplacement |