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An energy-based deep splitting method for the nonlinear filtering problem

Author

Listed:
  • Kasper Bågmark

    (Chalmers University of Technology and University of Gothenburg)

  • Adam Andersson

    (Chalmers University of Technology and University of Gothenburg
    Saab AB Radar Solutions)

  • Stig Larsson

    (Chalmers University of Technology and University of Gothenburg)

Abstract

The purpose of this paper is to explore the use of deep learning for the solution of the nonlinear filtering problem. This is achieved by solving the Zakai equation by a deep splitting method, previously developed for approximate solution of (stochastic) partial differential equations. This is combined with an energy-based model for the approximation of functions by a deep neural network. This results in a computationally fast filter that takes observations as input and that does not require re-training when new observations are received. The method is tested on four examples, two linear in one and twenty dimensions and two nonlinear in one dimension. The method shows promising performance when benchmarked against the Kalman filter and the bootstrap particle filter.

Suggested Citation

  • Kasper Bågmark & Adam Andersson & Stig Larsson, 2023. "An energy-based deep splitting method for the nonlinear filtering problem," Partial Differential Equations and Applications, Springer, vol. 4(2), pages 1-27, April.
    • Handle: RePEc:spr:pardea:v:4:y:2023:i:2:d:10.1007_s42985-023-00231-5
    DOI: 10.1007/s42985-023-00231-5
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    References listed on IDEAS

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    1. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    2. Brigo, Damiano & Hanzon, Bernard, 1998. "On some filtering problems arising in mathematical finance," Insurance: Mathematics and Economics, Elsevier, vol. 22(1), pages 53-64, May.
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