Diffusion maps particle filter

Lukas Forster, Alexander Schmidt, Walter Kellermann, Tal Shnitzer, Ronen Talmon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, we propose a new nonparametric filtering framework combining manifold learning and particle filtering. Diffusion maps, a nonparametric manifold learning method, is applied to obtain a parametric state-space model, inferring the state coordinates, their dynamics, as well as the function that links the state to the noisy observations, in a purely data-driven manner. Then, based on the inferred parametric model, a particle filter is devised, facilitating the processing of high-dimensional noisy observations without rigid prior model assumptions. We demonstrate the performance of the proposed approach in a simulation of a challenging tracking problem with noisy observations and a hidden model.

Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
ISBN (Electronic)9789082797039
StatePublished - Sep 2019
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: 2 Sep 20196 Sep 2019

Publication series

NameEuropean Signal Processing Conference


Conference27th European Signal Processing Conference, EUSIPCO 2019
CityA Coruna


  • Manifold learning
  • Nonlinear filtering
  • Nonparametric filtering
  • Sequential Markov chain Monte Carlo

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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