@InProceedings{8903123,
  author = {L. Forster and A. Schmidt and W. Kellermann and T. Shnitzer and R. Talmon},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Diffusion Maps Particle Filter},
  year = {2019},
  pages = {1-5},
  abstract = {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.},
  keywords = {learning (artificial intelligence);particle filtering (numerical methods);state-space methods;nonparametric filtering framework;particle filtering;nonparametric manifold learning method;parametric state-space model;inferred parametric model;high-dimensional noisy observations;diffusion map particle filter;Eigenvalues and eigenfunctions;Covariance matrices;Mathematical model;Noise measurement;Dynamical systems;Parametric statistics;Manifold learning;nonparametric filtering;non-linear filtering;sequential Markov chain Monte Carlo},
  doi = {10.23919/EUSIPCO.2019.8903123},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570531897.pdf},
}

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