Measurement-to-track association for nontraditional measurements
Data fusion algorithms must typically address not only kinematic issues - that is, target tracking - but also nonkinematics - for example, target identification, threat estimation, intent assessment, etc. Whereas kinematics involves traditional measurements such as radar detections, nonkinematics ty...
| Main Author: | |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/55247 |
| _version_ | 1848759572428226560 |
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| author | Mahler, Ronald |
| author_facet | Mahler, Ronald |
| author_sort | Mahler, Ronald |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Data fusion algorithms must typically address not only kinematic issues - that is, target tracking - but also nonkinematics - for example, target identification, threat estimation, intent assessment, etc. Whereas kinematics involves traditional measurements such as radar detections, nonkinematics typically involves non-traditional measurements such as quantized data, attributes, features, natural-language statements, and inference rules. The kinematic vs. nonkinematic chasm is often bridged by grafting some expert-system approach (fuzzy logic, Dempster-Shafer, rule-based inference) into a single- or multi-hypothesis multitarget tracking algorithm, using ad hoc methods. The purpose of this paper is to show that conventional measurement-to-track association theory can be directly extended to nontraditional measurements in a Bayesian manner. Concepts such as association likelihood, association distance, hypothesis probability, and global nearest-neighbor distance are defined, and explicit formulas are derived for specific kinds of nontraditional evidence. © 2011 IEEE. |
| first_indexed | 2025-11-14T10:02:01Z |
| format | Conference Paper |
| id | curtin-20.500.11937-55247 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:02:01Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-552472017-08-24T02:17:50Z Measurement-to-track association for nontraditional measurements Mahler, Ronald Data fusion algorithms must typically address not only kinematic issues - that is, target tracking - but also nonkinematics - for example, target identification, threat estimation, intent assessment, etc. Whereas kinematics involves traditional measurements such as radar detections, nonkinematics typically involves non-traditional measurements such as quantized data, attributes, features, natural-language statements, and inference rules. The kinematic vs. nonkinematic chasm is often bridged by grafting some expert-system approach (fuzzy logic, Dempster-Shafer, rule-based inference) into a single- or multi-hypothesis multitarget tracking algorithm, using ad hoc methods. The purpose of this paper is to show that conventional measurement-to-track association theory can be directly extended to nontraditional measurements in a Bayesian manner. Concepts such as association likelihood, association distance, hypothesis probability, and global nearest-neighbor distance are defined, and explicit formulas are derived for specific kinds of nontraditional evidence. © 2011 IEEE. 2011 Conference Paper http://hdl.handle.net/20.500.11937/55247 restricted |
| spellingShingle | Mahler, Ronald Measurement-to-track association for nontraditional measurements |
| title | Measurement-to-track association for nontraditional measurements |
| title_full | Measurement-to-track association for nontraditional measurements |
| title_fullStr | Measurement-to-track association for nontraditional measurements |
| title_full_unstemmed | Measurement-to-track association for nontraditional measurements |
| title_short | Measurement-to-track association for nontraditional measurements |
| title_sort | measurement-to-track association for nontraditional measurements |
| url | http://hdl.handle.net/20.500.11937/55247 |