A Tutorial on Bernoulli Filters: Theory, Implementation and Applications
Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive estimation of dynamic systems, recently emerged from the random set theoretical framework. The common feature of Bernoulli filters is that they are designed for stochastic dynamic systems which randomly swi...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/6652 |
| _version_ | 1848745139066896384 |
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| author | Ristic, B. Vo, Ba Tuong Vo, Ba-Ngu Farina, A. |
| author_facet | Ristic, B. Vo, Ba Tuong Vo, Ba-Ngu Farina, A. |
| author_sort | Ristic, B. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive estimation of dynamic systems, recently emerged from the random set theoretical framework. The common feature of Bernoulli filters is that they are designed for stochastic dynamic systems which randomly switch on and off. The applications are primarily in target tracking, where the switching process models target appearance or disappearance from the surveillance volume. The concept, however, is applicable to a range of dynamic phenomena, such as epidemics, pollution, social trends, etc. Bernoulli filters in general have no analytic solution and are implemented as particle filters or Gaussian sum filters. This tutorial paper reviews the theory of Bernoulli filters as well as their implementation for different measurement models. The theory is backed up by applications in sensor networks, bearings-only tracking, passive radar/sonar surveillance, visual tracking, monitoring/prediction of an epidemic and tracking using natural language statements. More advanced topics of smoothing, multi-target detection/tracking, parameter estimation and sensor control are briefly reviewed with pointers for further reading. |
| first_indexed | 2025-11-14T06:12:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-6652 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:12:36Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-66522017-09-13T14:37:52Z A Tutorial on Bernoulli Filters: Theory, Implementation and Applications Ristic, B. Vo, Ba Tuong Vo, Ba-Ngu Farina, A. Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive estimation of dynamic systems, recently emerged from the random set theoretical framework. The common feature of Bernoulli filters is that they are designed for stochastic dynamic systems which randomly switch on and off. The applications are primarily in target tracking, where the switching process models target appearance or disappearance from the surveillance volume. The concept, however, is applicable to a range of dynamic phenomena, such as epidemics, pollution, social trends, etc. Bernoulli filters in general have no analytic solution and are implemented as particle filters or Gaussian sum filters. This tutorial paper reviews the theory of Bernoulli filters as well as their implementation for different measurement models. The theory is backed up by applications in sensor networks, bearings-only tracking, passive radar/sonar surveillance, visual tracking, monitoring/prediction of an epidemic and tracking using natural language statements. More advanced topics of smoothing, multi-target detection/tracking, parameter estimation and sensor control are briefly reviewed with pointers for further reading. 2013 Journal Article http://hdl.handle.net/20.500.11937/6652 10.1109/TSP.2013.2257765 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | Ristic, B. Vo, Ba Tuong Vo, Ba-Ngu Farina, A. A Tutorial on Bernoulli Filters: Theory, Implementation and Applications |
| title | A Tutorial on Bernoulli Filters: Theory, Implementation and Applications |
| title_full | A Tutorial on Bernoulli Filters: Theory, Implementation and Applications |
| title_fullStr | A Tutorial on Bernoulli Filters: Theory, Implementation and Applications |
| title_full_unstemmed | A Tutorial on Bernoulli Filters: Theory, Implementation and Applications |
| title_short | A Tutorial on Bernoulli Filters: Theory, Implementation and Applications |
| title_sort | tutorial on bernoulli filters: theory, implementation and applications |
| url | http://hdl.handle.net/20.500.11937/6652 |