Tensor network Kalman filter for LTI systems

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Abstract

An extension of the Tensor Network (TN) Kalman filter [2], [3] for large scale LTI systems is presented in this paper. The TN Kalman filter can handle exponentially large state vectors without constructing them explicitly. In order to have efficient algebraic operations, a low TN rank is required. We exploit the possibility to approximate the covariance matrix as a TN with a low TN rank. This reduces the computational complexity for general SISO and MIMO LTI systems with TN rank greater than one significantly while obtaining an accurate estimation. Improvements of this method in terms of computational complexity compared to the conventional Kalman filter are demonstrated in numerical simulations for large scale systems.

Original languageEnglish
Title of host publicationProceedings of the 27th European Signal Processing Conference (EUSIPCO 2019)
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Number of pages5
ISBN (Electronic)978-9-0827-9703-9
DOIs
Publication statusPublished - 2019
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: 2 Sep 20196 Sep 2019

Conference

Conference27th European Signal Processing Conference, EUSIPCO 2019
CountrySpain
CityA Coruna
Period2/09/196/09/19

Keywords

  • Curse of dimensionality
  • Kalman filter
  • Large scale systems
  • LTI systems
  • MIMO
  • SISO
  • Tensor train
  • Tensors

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  • Cite this

    Gedon, D., Piscaer, P., Batselier, K., Smith, C., & Verhaegen, M. (2019). Tensor network Kalman filter for LTI systems. In Proceedings of the 27th European Signal Processing Conference (EUSIPCO 2019) IEEE. https://doi.org/10.23919/EUSIPCO.2019.8902976