Title :
A learning drift homotopy particle filter
Author :
Vasileios Maroulas;Kai Kang;Ioannis D. Schizas;Michael W. Berry
Author_Institution :
Dept of Math, University of Tennessee, Knoxville, Tennessee 37996
fDate :
7/1/2015 12:00:00 AM
Abstract :
In this paper, we design a learning drift homotopy particle filter algorithm. We employ the drift homotopy technique in the extra Markov Chain Monte Carlo move after the resampling step of the generic particle filter algorithm to efficiently resolve the degeneracy of the algorithm. In this work, we use the effective sample size as a learning parameter to control the levels of drift homotopy which need to be considered in each time step. The proposed algorithm adjusts the number of levels of drift homotopy and reduces its computational time without undermining the accuracy of estimation. We test the algorithm on two synthetic problems, a partially observed diffusion in a double well potential and a multi-target tracking setting.
Keywords :
"Approximation methods","Estimation","Heuristic algorithms","Monte Carlo methods","Particle filters","Markov processes","Hidden Markov models"
Conference_Titel :
Information Fusion (Fusion), 2015 18th International Conference on