Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking
In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/29075 |
| _version_ | 1848752706295955456 |
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| author | Papi, Francesco Bocquel, M. Podt, M. Boers, Y. |
| author_facet | Papi, Francesco Bocquel, M. Podt, M. Boers, Y. |
| author_sort | Papi, Francesco |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy. © 2014 IEEE. |
| first_indexed | 2025-11-14T08:12:53Z |
| format | Journal Article |
| id | curtin-20.500.11937-29075 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:12:53Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-290752017-09-13T15:23:55Z Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking Papi, Francesco Bocquel, M. Podt, M. Boers, Y. In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy. © 2014 IEEE. 2014 Journal Article http://hdl.handle.net/20.500.11937/29075 10.1109/TSP.2014.2321731 Institute of Electrical and Electronics Engineers Inc. restricted |
| spellingShingle | Papi, Francesco Bocquel, M. Podt, M. Boers, Y. Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking |
| title | Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking |
| title_full | Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking |
| title_fullStr | Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking |
| title_full_unstemmed | Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking |
| title_short | Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking |
| title_sort | fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking |
| url | http://hdl.handle.net/20.500.11937/29075 |