Name
Title | ||
|---|---|---|
An Iterative Nonlinear Filter Based on Posterior Distribution Approximation via Penalized Kullback-Leibler Divergence Minimization |
Abstract | ||
|---|---|---|
This letter deals with Gaussian approximation of complicated posterior distribution involved in the Bayesian paradigm for nonlinear dynamic systems. A general formulation for Gaussian approximation is first provided by equivalently representing posterior distribution as a Gaussian one with some constraint via embedding technique. In this work, it is specified as a penalized Kullback-Leibler divergence minimization problem. This minimization is solved for the expected Gaussian approximation by utilizing a pre-selected cubature rule and the conditional gradient method. Then, a novel iterative filter is developed for nonlinear dynamic systems. In addition, it is also proved to be optimal in linear cases and demonstrated to be effective through simulations. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LSP.2022.3169439 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Minimization, Gaussian approximation, Nonlinear filters, Maximum likelihood detection, Kalman filters, Linear programming, Time measurement, Bayesian paradigm, Gaussian approximation, iterative filter, Kullback-Leibler divergence minimization | Journal | 29 |
ISSN | Citations | PageRank |
1070-9908 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanfeng Hu | 1 | 0 | 0.68 |
| Liping Guo | 2 | 0 | 0.34 |
| Jie Zhou | 3 | 2103 | 190.17 |