Title :
Approximate conditional mean particle filter
Author :
Yee, Derek ; Reilly, James P. ; Kirubarajan, Thia
Author_Institution :
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, Ont.
Abstract :
We consider partially observed non-Gaussian dynamic state space models in which the process equation consists of a combination of linear and nonlinear states and the process noise for the nonlinear state update is distributed according to a mixture of Gaussians. In this paper, we solve a Bayesian filtering problem. The proposed filter is an efficient combination of the particle filter and the approximate conditional mean filter. Simulation results on a time-varying autoregressive signal demonstrate the effectiveness of the proposed algorithm
Keywords :
Bayes methods; Gaussian distribution; approximation theory; autoregressive processes; nonlinear filters; particle filtering (numerical methods); time-varying filters; Bayesian filtering problem; Gaussian mixture; conditional mean particle filter approximation; dynamic state space model; time-varying autoregressive signal; Bayesian methods; Decision support systems; Filtering; Gaussian distribution; Gaussian noise; Nonlinear equations; Particle filters; Signal processing algorithms; State-space methods; White noise;
Conference_Titel :
Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on
Conference_Location :
Novosibirsk
Print_ISBN :
0-7803-9403-8
DOI :
10.1109/SSP.2005.1628629
