Särkkä, S and Bunch, P and Godsill, SJ (2012) A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models. IFAC Proceedings Volumes (IFAC-PapersOnline), 16. pp. 506-511. ISSN 1474-6670Full text not available from this repository.
In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application. © 2012 IFAC.
|Uncontrolled Keywords:||Estimation algorithms Monte Carlo method Nonlinear systems Optimal estimation Stochastic systems|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||16 Jul 2015 13:23|
|Last Modified:||30 Nov 2015 13:41|