Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6958830 | Signal Processing | 2016 | 15 Pages |
Abstract
This work shows how a carefully designed instrumental distribution can improve the performance of a Markov chain Monte Carlo (MCMC) filter for systems with a high state dimension. We propose a special subgradient-based kernel from which candidate moves are drawn. This facilitates the implementation of the filtering algorithm in high dimensional settings using a remarkably small number of particles. We demonstrate our approach in solving a nonlinear non-Gaussian high-dimensional problem in comparison with a recently developed block particle filter and over a dynamic compressed sensing (l1 constrained) algorithm. The results show high estimation accuracy.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Avishy Y. Carmi, Lyudmila Mihaylova, François Septier,