A non-degenerate Rao-Blackwellised particle filter for estimating static parameters in dynamical models
2012 (English)In: Proceedings of the 16th IFAC Symposium on System Identification, 2012Conference paper, Presentation (Refereed)
The particle filter (PF) has emerged as a powerful tool for solving nonlinear and/or non-Gaussian filtering problems. When some of the states enter the model linearly, this can be exploited by using particles only for the "nonlinear" states and employing conditional Kalman filters for the "linear" states; this leads to the Rao-Blackwellised particle filter (RBPF). However, it is well known that the PF fails when the state of the model contains some static parameter. This is true also for the RBPF, even if the static states are marginalised analytically by a Kalman filter. The reason is that the posterior density of the static states is computed conditioned on the nonlinear particle trajectories, which are bound to degenerate over time. To circumvent this problem, we propose a method for targeting the posterior parameter density, conditioned on just the current nonlinear state. This results in an RBPF-like method, capable of recursive identification of nonlinear dynamical models with affine parameter dependencies.
Place, publisher, year, edition, pages
Particle Filtering/Monte Carlo Methods; Nonlinear System Identification; Recursive Identification
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-81271DOI: 10.3182/20120711-3-BE-2027.00184OAI: oai:DiVA.org:liu-81271DiVA: diva2:551267
16th IFAC Symposium on System Identification, Brussels, Belgium, July 11-13, 2012.
FunderSwedish Research Council