Title :
A robust particle filter for state estimation — with convergence results
Author :
Hu, Xiao-Li ; Schön, Thomas B. ; Ljung, Lennart
Author_Institution :
China Jiliang Univ., Hangzhou
Abstract :
Particle filters are becoming increasingly important and useful for state estimation in nonlinear systems. Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itself has been lacking. This contribution describes a general framework for particle filters for state estimation, as well as a robustified filter version. For this version a quite general convergence result is established. In particular, it is proved that the particle filter estimate convergences w.p.1 to the optimal estimate, as the number of particles tends to infinity.
Keywords :
Monte Carlo methods; nonlinear estimation; nonlinear systems; particle filtering (numerical methods); state estimation; nonlinear systems; optimal estimation; robust particle filter; sequential Monte Carlo methods; state estimation; Convergence; Equations; Filtering; Noise measurement; Nonlinear dynamical systems; Particle filters; Robustness; State estimation; Stochastic processes; Time measurement;
Conference_Titel :
Decision and Control, 2007 46th IEEE Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-1497-0
Electronic_ISBN :
0191-2216
DOI :
10.1109/CDC.2007.4434208