By Topic

Nonlinear state estimation by evolution strategies based particle filters

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Uosaki, K. ; Dept. of Inf. & Phys. Sci., Osaka Univ., Japan ; Kimura, Y. ; Hatanaka, T.

There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. By recognizing the similarities and the difference of the processes between the particle filters and evolution strategies, a new filter, evolution strategies based particle filter, is proposed to circumvent this difficulty and to improve the performance. The applicability of the proposed idea is illustrated by numerical studies.

Published in:

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on  (Volume:3 )

Date of Conference:

8-12 Dec. 2003