Garcia-Fernandez, Angel F 
ORCID: 0000-0002-6471-8455 and Svensson, Lennart
(2015)
Gaussian MAP Filtering Using Kalman Optimization
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 60 (5).
pp. 1336-1349.
ISSN 0018-9286, 1558-2523
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MAP_KF_final_submission.pdf - Author Accepted Manuscript Download (1MB) | Preview |
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bayesian nonlinear filtering, Kalman filter, MAP estimation, optimization |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 22 Jan 2020 09:58 |
| Last Modified: | 22 May 2026 23:04 |
| DOI: | 10.1109/TAC.2014.2372909 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3071560 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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