27 October 2013 A particle-inspired Monte Carlo tree estimation method in Bayesian filtering
Author Affiliations +
Proceedings Volume 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing; 89200C (2013) https://doi.org/10.1117/12.2031337
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
A particle-inspired Monte Carlo tree estimation method is proposed to avoid repeating similar simulation and handle the depletion problem in particle filter. Under the inspiration of particles, the method divides the state-space recursively in a top-down manner to form a tree structure that each node in the tree is corresponding to a sub-space. Particles are allocated to the corresponding terminal node during the procedure. Certain size of minimal sub-space or piece is specified to terminate the dividing. Each piece is corresponding to a leaf-node of the tree structure and the prediction probability density in it is approximated by the proportion of its particles in total particles. Instead of importance sampling for each particle, the method takes uniformly random measurements to compute the posterior probability density in each piece. As a result, the method is applied to growth model and has better performance in high SNR environments compared with the Sampling Importance Resampling method.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hong Wu, Hong Wu, Dehua Li, Dehua Li, Qingguang Li, Qingguang Li, Hui Shen, Hui Shen, } "A particle-inspired Monte Carlo tree estimation method in Bayesian filtering", Proc. SPIE 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing, 89200C (27 October 2013); doi: 10.1117/12.2031337; https://doi.org/10.1117/12.2031337
PROCEEDINGS
8 PAGES


SHARE
RELATED CONTENT

Particle flow for nonlinear filters with log-homotopy
Proceedings of SPIE (April 16 2008)
Influence of a non Gaussian state model on the position...
Proceedings of SPIE (December 28 2007)
Spline filter for nonlinear/non-Gaussian Bayesian tracking
Proceedings of SPIE (September 25 2007)
Nonlinear filters with particle flow
Proceedings of SPIE (September 04 2009)
Nonlinear filters with log-homotopy
Proceedings of SPIE (September 25 2007)

Back to Top