Gaussian MAP Filtering Using Kalman Optimization
© 1963-2012 IEEE. This paper deals with the update step of Gaussian MAP filtering. In this framework, we seek a Gaussian approximation to the posterior probability density function (PDF) whose mean is given by the maximum a posteriori (MAP) estimator. We propose two novel optimization algorithms whi...
| Main Authors: | , |
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| Format: | Journal Article |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/6909 |
| _version_ | 1848745213416177664 |
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| author | García-Fernández, Angel Svensson, Lennart |
| author_facet | García-Fernández, Angel Svensson, Lennart |
| author_sort | García-Fernández, Angel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 1963-2012 IEEE. This paper deals with the update step of Gaussian MAP filtering. In this framework, we seek a Gaussian approximation to the posterior probability density function (PDF) whose mean is given by the maximum a posteriori (MAP) estimator. We propose two novel optimization algorithms which are quite suitable for finding the MAP estimate although they can also be used to solve general optimization problems. These are based on the design of a sequence of PDFs that become increasingly concentrated around the MAP estimate. The resulting algorithms are referred to as Kalman optimization (KO) methods. We also provide the important relations between these KO methods and their conventional optimization algorithms (COAs) counterparts, i.e., Newton's and Levenberg-Marquardt algorithms. Our simulations indicate that KO methods are more robust than their COA equivalents. |
| first_indexed | 2025-11-14T06:13:47Z |
| format | Journal Article |
| id | curtin-20.500.11937-6909 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:13:47Z |
| publishDate | 2015 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-69092017-09-13T14:42:51Z Gaussian MAP Filtering Using Kalman Optimization García-Fernández, Angel Svensson, Lennart © 1963-2012 IEEE. This paper deals with the update step of Gaussian MAP filtering. In this framework, we seek a Gaussian approximation to the posterior probability density function (PDF) whose mean is given by the maximum a posteriori (MAP) estimator. We propose two novel optimization algorithms which are quite suitable for finding the MAP estimate although they can also be used to solve general optimization problems. These are based on the design of a sequence of PDFs that become increasingly concentrated around the MAP estimate. The resulting algorithms are referred to as Kalman optimization (KO) methods. We also provide the important relations between these KO methods and their conventional optimization algorithms (COAs) counterparts, i.e., Newton's and Levenberg-Marquardt algorithms. Our simulations indicate that KO methods are more robust than their COA equivalents. 2015 Journal Article http://hdl.handle.net/20.500.11937/6909 10.1109/TAC.2014.2372909 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC restricted |
| spellingShingle | García-Fernández, Angel Svensson, Lennart Gaussian MAP Filtering Using Kalman Optimization |
| title | Gaussian MAP Filtering Using Kalman Optimization |
| title_full | Gaussian MAP Filtering Using Kalman Optimization |
| title_fullStr | Gaussian MAP Filtering Using Kalman Optimization |
| title_full_unstemmed | Gaussian MAP Filtering Using Kalman Optimization |
| title_short | Gaussian MAP Filtering Using Kalman Optimization |
| title_sort | gaussian map filtering using kalman optimization |
| url | http://hdl.handle.net/20.500.11937/6909 |