DocumentCode :
3598396
Title :
Importance sampling applied to Pincus maximization for particle filter MAP estimation
Author :
Saha, Saikat ; Gustafsson, Fredrik
Author_Institution :
Dept. of Electr. Eng., Linkoping Univ., Linköping, Sweden
fYear :
2012
Firstpage :
114
Lastpage :
120
Abstract :
Sequential Monte Carlo (SMC), or Particle Filters (PF), approximate the posterior distribution in nonlinear filtering arbitrarily well, but the problem how to compute a state estimate is not always straightforward. For multimodal posteriors, the maximum a posteriori (MAP) estimate is a logical choice, but it is not readily available from the SMC output. In principle, the MAP can be obtained by maximizing the posterior density obtained e.g. by the particle based approximation of the Chapman-Kolmogorov equation. However, this posterior is a mixture distribution with many local maxima, which makes the optimization problem very hard. We suggest an algorithm for estimating the MAP using the global optimization principle of Pincus and subsequently outline the frameworks for estimating the filter and marginal smoother MAP of a dynamical system from the SMC output.
Keywords :
Monte Carlo methods; maximum likelihood estimation; nonlinear filters; particle filtering (numerical methods); Chapman-Kolmogorov equation; Pincus maximization; SMC output; dynamical system; global optimization principle; importance sampling; marginal smoother MAP; maximum a posteriori estimate; multimodal posteriors; nonlinear filtering; particle based approximation; particle filter MAP estimation; posterior distribution; sequential Monte Carlo; Approximation methods; Equations; Estimation; Mathematical model; Monte Carlo methods; Optimization; Proposals; global optimization; maximum a posteriori; particle filter; particle smoother;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2
Type :
conf
Filename :
6289794
Link To Document :
بازگشت