TR2020-171

Exploiting linear substructure in linear regression Kalman filters


    •  Greiff, M., Robertsson, A., Berntorp, K., "Exploiting linear substructure in linear regression Kalman filters", IEEE Annual Conference on Decision and Control (CDC), DOI: 10.1109/​CDC42340.2020.9304191, December 2020.
      BibTeX TR2020-171 PDF
      • @inproceedings{Greiff2020dec,
      • author = {Greiff, Marcus and Robertsson, Anders and Berntorp, Karl},
      • title = {Exploiting linear substructure in linear regression Kalman filters},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2020,
      • month = dec,
      • doi = {10.1109/CDC42340.2020.9304191},
      • url = {https://www.merl.com/publications/TR2020-171}
      • }
  • MERL Contact:
  • Research Areas:

    Control, Signal Processing

Abstract:

We exploit knowledge of linear substructure in the linear-regression Kalman filters (LRKFs) to simplify the problem of moment matching. The theoretical results yield quantifiable and significant computational speedups at no cost of estimation accuracy, assuming partially linear estimation models. The results apply to any symmetrical LRKF, and reductions in computational complexity are stated as a function of the cubature rule, the number of linear and nonlinear states in the estimation model respectively. The implications for the filtering problem are illustrated by several numerical examples.

 

  • Related Publication

  •  Greiff, M., Robertsson, A., Berntorp, K., "Exploiting Linear Substructure in LRKFs", arXiv, September 2020.
    BibTeX arXiv
    • @article{Greiff2020sep,
    • author = {Greiff, Marcus and Robertsson, Anders and Berntorp, Karl},
    • title = {Exploiting Linear Substructure in LRKFs},
    • journal = {arXiv},
    • year = 2020,
    • month = sep,
    • url = {https://arxiv.org/abs/2009.07571}
    • }