Bayesian unified registration and tracking
Multitarget detection and tracking algorithms typically presume that sensors are spatially registered - i.e., that all sensor states are precisely specified with respect to some common coordinate system. In actuality, sensor observations may be contaminated by unknown spatial misregistration biases....
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/55826 |
| _version_ | 1848759717509201920 |
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| author | Mahler, Ronald El-Fallah, A. |
| author_facet | Mahler, Ronald El-Fallah, A. |
| author_sort | Mahler, Ronald |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Multitarget detection and tracking algorithms typically presume that sensors are spatially registered - i.e., that all sensor states are precisely specified with respect to some common coordinate system. In actuality, sensor observations may be contaminated by unknown spatial misregistration biases. This paper demonstrates that these biases can be estimated by exploiting the data collected from a sufficiently large number of unknown targets, in a unified methodology in which sensor registration and multitarget tracking are performed jointly in a fully unified fashion. We show how to (1) model single-sensor bias, (2) integrate the biased sensors into a single probabilistic multiplatform-multisensor-multitarget system, (3) construct the optimal solution to the joint registration/tracking problem, and (4) devise a principled computational approximation of this optimal solution. The approach does not presume the availability of GPS or other inertial information. © 2011 SPIE. |
| first_indexed | 2025-11-14T10:04:19Z |
| format | Conference Paper |
| id | curtin-20.500.11937-55826 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:04:19Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-558262017-09-13T16:11:11Z Bayesian unified registration and tracking Mahler, Ronald El-Fallah, A. Multitarget detection and tracking algorithms typically presume that sensors are spatially registered - i.e., that all sensor states are precisely specified with respect to some common coordinate system. In actuality, sensor observations may be contaminated by unknown spatial misregistration biases. This paper demonstrates that these biases can be estimated by exploiting the data collected from a sufficiently large number of unknown targets, in a unified methodology in which sensor registration and multitarget tracking are performed jointly in a fully unified fashion. We show how to (1) model single-sensor bias, (2) integrate the biased sensors into a single probabilistic multiplatform-multisensor-multitarget system, (3) construct the optimal solution to the joint registration/tracking problem, and (4) devise a principled computational approximation of this optimal solution. The approach does not presume the availability of GPS or other inertial information. © 2011 SPIE. 2011 Conference Paper http://hdl.handle.net/20.500.11937/55826 10.1117/12.885145 restricted |
| spellingShingle | Mahler, Ronald El-Fallah, A. Bayesian unified registration and tracking |
| title | Bayesian unified registration and tracking |
| title_full | Bayesian unified registration and tracking |
| title_fullStr | Bayesian unified registration and tracking |
| title_full_unstemmed | Bayesian unified registration and tracking |
| title_short | Bayesian unified registration and tracking |
| title_sort | bayesian unified registration and tracking |
| url | http://hdl.handle.net/20.500.11937/55826 |