Repository landing page
Subgradient-based Markov Chain Monte Carlo particle methods for discrete-time nonlinear filtering
Abstract
International audienceThis 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- info:eu-repo/semantics/article
- Journal articles
- Markov chain Monte Carlo methods
- High dimensional systems
- Compressed sensing
- L1 optimisation
- Filtering
- [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
- [STAT.AP]Statistics [stat]/Applications [stat.AP]
- [STAT.CO]Statistics [stat]/Computation [stat.CO]
- [STAT.ME]Statistics [stat]/Methodology [stat.ME]