Iterated statistical linear regression for Bayesian updates
© 2014 International Society of Information Fusion.This paper deals with Gaussian approximations to the posterior probability density function (PDF) in Bayesian nonlinear filtering. In this setting, using sigma-point based approximations to the Kalman filter (KF) recursion is a prominent approach. I...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/53890 |
| _version_ | 1848759252724744192 |
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| author | Garcia Fernandez, Angel Svensson, L. Morelande, M. |
| author_facet | Garcia Fernandez, Angel Svensson, L. Morelande, M. |
| author_sort | Garcia Fernandez, Angel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2014 International Society of Information Fusion.This paper deals with Gaussian approximations to the posterior probability density function (PDF) in Bayesian nonlinear filtering. In this setting, using sigma-point based approximations to the Kalman filter (KF) recursion is a prominent approach. In the update step, the sigma-point KF approximations are equivalent to performing the statistical linear regression (SLR) of the (nonlinear) measurement function with respect to the prior PDF. In this paper, we indicate that the SLR of the measurement function with respect to the posterior is expected to provide better results than the SLR with respect to the prior. The resulting filter is referred to as the posterior linearisation filter (PLF). In practice, the exact PLF update is intractable but can be efficiently approximated by carrying out iterated SLRs based on sigma-point approximations. On the whole, the resulting filter, the iterated PLF (IPLF), is expected to outperform all sigma-point KF approximations as demonstrated by numerical simulations. |
| first_indexed | 2025-11-14T09:56:56Z |
| format | Conference Paper |
| id | curtin-20.500.11937-53890 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:56:56Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-538902017-06-23T03:01:48Z Iterated statistical linear regression for Bayesian updates Garcia Fernandez, Angel Svensson, L. Morelande, M. © 2014 International Society of Information Fusion.This paper deals with Gaussian approximations to the posterior probability density function (PDF) in Bayesian nonlinear filtering. In this setting, using sigma-point based approximations to the Kalman filter (KF) recursion is a prominent approach. In the update step, the sigma-point KF approximations are equivalent to performing the statistical linear regression (SLR) of the (nonlinear) measurement function with respect to the prior PDF. In this paper, we indicate that the SLR of the measurement function with respect to the posterior is expected to provide better results than the SLR with respect to the prior. The resulting filter is referred to as the posterior linearisation filter (PLF). In practice, the exact PLF update is intractable but can be efficiently approximated by carrying out iterated SLRs based on sigma-point approximations. On the whole, the resulting filter, the iterated PLF (IPLF), is expected to outperform all sigma-point KF approximations as demonstrated by numerical simulations. 2014 Conference Paper http://hdl.handle.net/20.500.11937/53890 restricted |
| spellingShingle | Garcia Fernandez, Angel Svensson, L. Morelande, M. Iterated statistical linear regression for Bayesian updates |
| title | Iterated statistical linear regression for Bayesian updates |
| title_full | Iterated statistical linear regression for Bayesian updates |
| title_fullStr | Iterated statistical linear regression for Bayesian updates |
| title_full_unstemmed | Iterated statistical linear regression for Bayesian updates |
| title_short | Iterated statistical linear regression for Bayesian updates |
| title_sort | iterated statistical linear regression for bayesian updates |
| url | http://hdl.handle.net/20.500.11937/53890 |