14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper ThSP2.1

Gordon, Neil (DSTO)

Sequential Importance Resampling for State and Parameter Estimation

Scheduled for presentation during the Semi-Plenary Session "Semi-Plenary IV" (ThSP2), Thursday, March 30, 2006, 14:00−15:00, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on September 24, 2018

Keywords Bayesian Methods, Particle Filtering/Monte Carlo Methods


Certain ``computational Bayesian'' methods have recently had major effect in areas of applied statistics, econometrics and signal processing. Some of these draw on Metropolis-Hastings type methods to allow empirical sampling from rather arbitrary conditional distributions. This talk will concentrate on a related but more focussed topic of empirical sampling from the measurement and time updates of the Chapman-Kolmogorov equations governing optimal state estimation. These methods have attendant implications for nonlinear system parameter estimation.