The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations
It is shown analytically that the multi-target multi- Bernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets. To reduce the cardinality bias, a novel multi-Bernoulli approximation to the multi-target Bayes recursion is derived. Under the same assumptions as...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Published: |
IEEE
2009
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/39623 |
| _version_ | 1848755641637666816 |
|---|---|
| author | Vo, Ba Tuong Vo, Ba-Ngu Cantoni, Antonio |
| author_facet | Vo, Ba Tuong Vo, Ba-Ngu Cantoni, Antonio |
| author_sort | Vo, Ba Tuong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | It is shown analytically that the multi-target multi- Bernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets. To reduce the cardinality bias, a novel multi-Bernoulli approximation to the multi-target Bayes recursion is derived. Under the same assumptions as the MeMBer recursion, the proposed recursion is unbiased. In addition, a sequential Monte Carlo (SMC) implementation (for generic models) and a Gaussian mixture (GM) implementation (for linear Gaussian models) are proposed. The latter is also extended to accommodate mildly nonlinear models by linearization and the unscented transform. |
| first_indexed | 2025-11-14T08:59:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-39623 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:59:32Z |
| publishDate | 2009 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-396232017-09-13T14:30:06Z The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations Vo, Ba Tuong Vo, Ba-Ngu Cantoni, Antonio random sets tracking multi-Bernoulli point processes Estimation finite set statistics It is shown analytically that the multi-target multi- Bernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets. To reduce the cardinality bias, a novel multi-Bernoulli approximation to the multi-target Bayes recursion is derived. Under the same assumptions as the MeMBer recursion, the proposed recursion is unbiased. In addition, a sequential Monte Carlo (SMC) implementation (for generic models) and a Gaussian mixture (GM) implementation (for linear Gaussian models) are proposed. The latter is also extended to accommodate mildly nonlinear models by linearization and the unscented transform. 2009 Journal Article http://hdl.handle.net/20.500.11937/39623 10.1109/TSP.2008.2007924 IEEE fulltext |
| spellingShingle | random sets tracking multi-Bernoulli point processes Estimation finite set statistics Vo, Ba Tuong Vo, Ba-Ngu Cantoni, Antonio The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations |
| title | The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations |
| title_full | The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations |
| title_fullStr | The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations |
| title_full_unstemmed | The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations |
| title_short | The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations |
| title_sort | cardinality balanced multi-target multi-bernoulli filter and its implementations |
| topic | random sets tracking multi-Bernoulli point processes Estimation finite set statistics |
| url | http://hdl.handle.net/20.500.11937/39623 |