Subgradient-based Markov Chain Monte Carlo particle methods for discrete-time nonlinear filtering

Avishy Y. Carmi, Lyudmila Mihaylova, François Septier

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish
Pages (from-to)532-536
Number of pages5
JournalSignal Processing
StatePublished - 1 Mar 2016


  • Compressed sensing
  • Filtering
  • High dimensional systems
  • L1 optimisation
  • Markov chain Monte Carlo methods

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


Dive into the research topics of 'Subgradient-based Markov Chain Monte Carlo particle methods for discrete-time nonlinear filtering'. Together they form a unique fingerprint.

Cite this