Improving the particle filter for high-dimensional problems using artificial process noise

The particle filter is one of the most successful methods for state inference and identification of general non-linear and non-Gaussian models. However, standard particle filters suffer from degeneracy of the particle weights for high-dimensional problems. We propose a method for improving the performance of the particle filter for certain challenging state space models, with implications for high-dimensional inference. First we approximate the model by adding artificial process noise in an additional state update, then we design a proposal that combines the standard and the locally optimal proposal. This results in a bias-variance trade-off, where adding more noise reduces the variance of the estimate but increases the model bias. The performance of the proposed method is evaluated on a linear Gaussian state space model and on the non-linear Lorenz’96 model. For both models we observe a significant improvement in performance over the standard particle filter.

A. Wigren, L.M. Murray, F. Lindsten (2018). <a href="https://indii.org/research/improving-the-particle-filter-for-high-dimensional-problems/">Improving the particle filter for high-dimensional problems using artificial process noise</a>. <em>18th IFAC Symposium on System Identification (SYSID 2018)</em>. 

@Article{,
  title = {Improving the particle filter for high-dimensional problems using artificial process noise},
  author = {Anna Wigren and Lawrence M. Murray and Fredrik Lindsten},
  journal = {18th IFAC Symposium on System Identification (SYSID 2018)},
  year = {2018},
  url = {https://arxiv.org/abs/1801.07000}
}

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